Abstract by steven yiu

Forecasting women's apparel sales using mathematical modeling Celia Frank; Ashish Garg; Amar Raheja; Les Sztandera International Journal of Clothing Science and Technology; 2003; 15, 2; ABI/INFORM Global pg. 107

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

Expert Systems with Applications 36 (2009) 8900–8909

Contents lists available at ScienceDirect

Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa

Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard Ufuk Cebeci* Department of Industrial Engineering, Istanbul Technical University, Macka, Istanbul 34367, Turkey

a r t i c l e Keywords: ERP system Fuzzy AHP Textile Balanced scorecard Request for proposal

i n f o

a b s t r a c t An enterprise resource planning system (ERP) is the information backbone of a company that integrates and automates all business operations. It is a critical issue to select the suitable ERP system which meets all the business strategies and the goals of the company. This study presents an approach to select a suitable ERP system for textile industry. Textile companies have some difficulties to implement ERP systems such as variant structure of products, production variety and unqualified human resources. At first, the vision and the strategies of the organization are checked by using balanced scorecard. According to the company’s vision, strategies and KPIs, we can prepare a request for proposal. Then ERP packages that do not meet the requirements of the company are eliminated. After strategic management phase, the proposed methodology gives advice before ERP selection. The criteria were determined and then compared according to their importance. The rest ERP system solutions were selected to evaluate. An external evaluation team consisting of ERP consultants was assigned to select one of these solutions according to the predetermined criteria. In this study, the fuzzy analytic hierarchy process, a fuzzy extension of the multi-criteria decision-making technique AHP, was used to compare these ERP system solutions. The methodology was applied for a textile manufacturing company. Ó 2008 Elsevier Ltd. All rights reserved.

1. Introduction ERP systems are becoming more necessary for almost every firm to improve the competitiveness. According to the success of the implementation of ERP system; companies can obtain a competitive advantage in the global market rapidly. Over the past decade, many ERP projects have resulted in substantial tangible and intangible improvements in a variety of areas for the organizations (Davenport, 2000; Umble, Haft, & Umble, 2003; Yusuf, Gunasekaranb, & Abthorpe, 2004). However, there are a number of examples where organizations were not successful in reaping the potential benefits that motivated them to make large investments in ERP implementations (Davenport, 2000; Umble et al., 2003). Implementations of ERP systems are one of the most difficult investment projects because of the complexity, high cost and adaptation risks. Companies have spent billions of dollars and used numerous amounts of man-hours for installing elaborate ERP software systems (Yusuf et al., 2004). A successful ERP project involves selecting an ERP software system and co-operative vendor, implementing this system, managing business processes change and examining the practicality of the system (Wei & Wang, 2004). Karsak and Özogul (2009) presented a novel deci* Tel.: +90 212 2931300; fax: +90 212 2407260. E-mail addresses: cebeciu@itu.edu.tr, ufuk_cebeci@yahoo.com 0957-4174/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2008.11.046

sion framework for ERP software selection, employing quality function deployment, fuzzy linear regression and zero–one goal programming. Teltumbde (2000) proposed a methodology based on the nominal group technique and the AHP for evaluating ERP systems. Chang et al. (2008) proposed a neural network evaluation model for ERP performance from SCM perspective. The survey data was gathered from a transnational textile firm in Taiwan (Table 4). Determining the best ERP software that fits with the organizational necessity and criteria, is the first step of tedious implementation process. Hence, selecting a suitable ERP system is an extremely difficult and critical decision for managers. An unsuitable selection can significantly affect not only the success of the implementation but also performance of the company. However, many companies install their ERP systems hurriedly without fully understanding the implications for their business or the need for compatibility with overall organizational goals and strategies (Hicks & Stecke, 1995). The result of this hasty approach is failed projects or weak systems whose logic conflicts with organizational goals. This paper aims: to manage the early stages of ERP selection according to the vision and strategies by using balanced scorecard and to provide an analytical tool to select the most suitable ERP software for textile industry.

ARTICLE IN PRESS

Robotics and Computer-Integrated Manufacturing 24 (2008) 174–186 www.elsevier.com/locate/rcim

Fuzzy logic path planning for the robotic placement of fabrics on a work table G.T. Zoumponos, N.A. Aspragathos Mechanical & Aeronautics Engineering Department, University of Patras, 26500 Patra, Greece Received 2 January 2006; received in revised form 2 August 2006; accepted 2 October 2006

Abstract In this paper, an innovative fuzzy logic approach for the robotic laying of fabrics on a work table and based on fuzzy sets is presented. Through handling experiments the solution domain for the path of the robotic gripper is determined, the handling parameters are identified and implicit knowledge is accumulated. Then a proper scheme for the data acquisition is formed and a path-planning algorithm based on fuzzy logic is developed. Due to conflicts and inaccuracies of the acquired data, a subtractive clustering algorithm is used, to identify the proper clusters for the two developed fuzzy systems, with the first employing the clusters as rules and the second a neurofuzzy system initialised by the implicit knowledge and trained via back-propagation. Finally, the effectiveness of the two path-planning systems is investigated in an experimental stage where the robot successfully places on a table fabrics of a variety of materials and sizes. r 2006 Elsevier Ltd. All rights reserved. Keywords: Fabric handling; Fuzzy systems; Subtractive clustering; Motion planning

1. Introduction In the industries where non-rigid materials are processed, like cloth or automobile seats making, the degree of automation is still low. The labour-intensive processes in manufacturing increase the production cost significantly in the developed countries, so it is very urgent to develop flexible manufacturing systems by advancing the robot intelligence control. Saadat and Nan [1] conducted a survey of 96 published key research papers regarding the manipulation of flexible materials underlining the importance of the automation. A 76% of those publications deals with sheet materials and 58% of this percentage is related to the garment industry, whereas the rest is distributed almost equally among the aerospace, automotive and shoe/ leather industries. This survey shows the importance of automation for the manipulation of non-rigid materials in general and particularly the handling of highly flexible sheet materials in new relevant processes emerging in the Corresponding author. Tel.: +30 2610 997268; fax: +30 2610 997212.

E-mail addresses: zoumpoko@mech.upatras.gr (G.T. Zoumponos), asprag@mech.upatras.gr (N.A. Aspragathos). 0736-5845/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.rcim.2006.10.001

aircraft and automotive industry. Since the apparel industry is still labour-intensive, automated solutions should be found for manually performed tasks and operations and the operators-workers’ contribution should be restricted to supervising the progress of the production and thus reduce signiﬁcantly the production cost. The placement of a ply of a non-rigid material, such as fabric or leather, on a work table is nowadays a worker’s task, despite some attempts made to automate this task. The placement task includes the laying of a single ply on top of a work surface, its laying on top of another ply to be sewn with and the folding of the ply on to itself, with accuracy and without the appearance of wrinkles and loops. Such tasks being carried out by a robot presents some complications, due to the nature of the materials to be handled. Non-rigid materials, as the term denotes, are materials whose bending rigidity is quite low, and thus large deformations appear even when low bending forces are applied such as their own weight. In particular, a textile fabric is a very complex non-linear mechanical system whose shape, orientation, physical and mechanical properties vary almost unpredictably. It is impossible to obtain ‘‘closed form’’ mathematical solutions for the behaviour of

ARTICLE IN PRESS Int. J. Production Economics 114 (2008) 376– 387

Contents lists available at ScienceDirect

Int. J. Production Economics journal homepage: www.elsevier.com/locate/ijpe

Genetic optimization of fabric utilization in apparel manufacturing W.K. Wong , S.Y.S. Leung Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong

a r t i c l e i n f o

abstract

Article history: Received 22 May 2006 Accepted 14 February 2008 Available online 25 March 2008

In apparel manufacturing, cut order planning (COP) plays a signiﬁcant role in managing the cost of materials as fabric usually occupies more than 50% of the total manufacturing cost. Following the details of retail orders in terms of quantity, size and colour, COP seeks to minimize the total manufacturing costs by developing feasible cutting order plans with respect to material, machine and labour. In this paper, a genetic optimized decisionmaking model using adaptive evolutionary strategies is proposed to assist the production management of the apparel industry in the decision-making process of COP in which a new encoding method with a shortened binary string is devised. Four sets of real production data were collected to validate the proposed decision support method. The experimental results demonstrate that the proposed method can reduce both the material costs and the production of additional garments while satisfying the time constraints set by the downstream sewing department. Although the total operation time used is longer than that using industrial practice, the great beneﬁts obtained by less fabric cost and extra quantity of garments planned and produced largely outweigh the longer operation time required. & 2008 Elsevier B.V. All rights reserved.

Keywords: Evolutionary strategies Optimization Decision support Resource utilization

1. Introduction In today’s apparel industry, fashion products require a signiﬁcant amount of customization due to differences in body measurements, diverse preferences on style and replacement cycle. It is necessary for apparel supply chains to be responsive to the ever-changing fashion markets by producing smaller jobs in order to provide customers with timely and customized fashion products. In apparel supply chains, fabric is the single largest material in the cost of a garment; approximately 50–60% of the manufacturing cost can be attributed to fabric. Apart from the fabric material, labour and factory operation costs have also been continuously increasing while the selling price of apparel merchandise have been decreasing. Adopting quick response strategies to manufacture and deliver apparel products to the retailers while

Corresponding author. Tel.: +852 2766 6471; fax: +852 2773 1432.

maximizing the fabric utilization rate (in other words, minimizing the material cost) and minimizing the labour and manufacturing cost becomes a great challenge to apparel manufacturers. 1.1. Cut order planning Cut order planning (COP) is the ﬁrst stage in the production workﬂow of a typical apparel manufacturing company, as shown in Fig. 1. It is a planning process to determine how many markers are needed, how many of each size of garment should be in each marker and the number of fabric plies that will be cut from each marker. Marker is the output of the process of marker planning, which is the operation following the COP. Fig. 2 illustrates a marker planning process using commercial computing to arrange all patterns of the component parts of one or more garments on a piece of marker paper, as shown in Fig. 3. Following marker planning, the third operation is fabric spreading, which is a process by which fabric pieces are superimposed to become a fabric lay on a cutting

The current issue and full text archive of this journal is available at www.emeraldinsight.com/0955-6222.htm

Investigating the development of digital patterns for customized apparel Yunchu Yang and Weiyuan Zhang

The development of digital patterns 167

Fashion Institute, Donghua University, Shanghai, People’s Republic of China, and

Cong Shan Fashion Institute, Donghua University, Shanghai, People’s Republic of China and Shanghai University of Engineering Science, Shanghai, People’s Republic of China Abstract Purpose – The paper aims to provide an overview of the area of digital pattern developing for customized apparel. Design/methodology/approach – The paper outlines several methods of digital pattern developing for customized apparel, and discusses the principles, characters and applications. Digital pattern developing process has two paths. One path develops apparel according to traditional 2D pattern-making technology. There are three methods: parametric design, traditional grading technique, and pattern generating based on artificial intelligence (AI). Another path develops pattern through surface flattening directly from individual 3D apparel model. Findings – For parametric method, it can improve greatly the efficiency of pattern design or pattern alteration. However, the development and application of parametric Computer-Aided-Design (CAD) systems in apparel industry are difficult, because apparel pattern has fewer laws in graphical structure. For grading technique, it is the most practical method because of its simple theory, with which pattern masters are familiar. But these methods require users with higher experience. Creating expert pattern system based on AI can reduce the experience requirements. Meanwhile, a great deal of experiments should be conducted for each garment with different style to create their knowledge databases. For 3D CAD technology, two methods of surface flattening have been outlined, namely geometry flattening and physical flattening. But many improvements should be done if the 3D CAD systems are applied in apparel mass customization. Originality/value – The paper provides information of value to the future research on developing a practical made-to-measure apparel pattern system. Keywords Customization, Clothing, Parametric measures, Artificial intelligence, Flatness measurement Paper type Viewpoint

Introduction In today’s apparel market, most of consumers desire to personalize the style, fit and color of the clothes. They require high-quality customized products at low prices with faster delivery. With this sort of consumer interest in mind, the concept of mass customization emerged in the late 1980s (Seung-Eun and Chen, 2000). Mass customization is a hybrid of mass production and customization and is a new manufacturing trend. It is an effective competing strategy for maximizing customers’ satisfaction and minimizing inventory costs. In the book, Mass Customization, Pine (1993) defined mass customization as “the mass production of individually customized goods and services”. Pine stated that information technology and automation are prerequisite of implementing mass customization because they constitute

International Journal of Clothing Science and Technology Vol. 19 No. 3/4, 2007 pp. 167-177 q Emerald Group Publishing Limited 0955-6222 DOI 10.1108/09556220710741632

E-BUSINESS IN APPAREL RETAILING INDUSTRY – CRITICAL ISSUES Virpi Kristiina Tuunainen Matti Rossi Helsinki School of Economics P.O. Box 1210 FIN-00101 Helsinki Finland email: {tuunaine|rossi}@hkkk.fi

ABSTRACT The apparel industry has, like most other industries quickly started using the Internet to gain improvements in the efficiency and effectiveness of operations and marketing. In this report we briefly overview the developments of electronic commerce in apparel industry. We try to develop a framework for choosing the right technology and development options based on the business model and business orientation chosen. We illustrate the framework by four case companies, which have adapted different basic strategies and business models. The cases include companies with traditional operations with also physical retail outlets, as well as companies operating only on the Internet. There are still a number of unresolved problems related both to consumer-oriented e-commerce in general and to apparel industry in particular. Nevertheless, consumers are increasingly using the Internet to do extensive amount of research on products and fashion trends before purchasing through any media, also making more and more online purchases

1. INTRODUCTION The apparel industry has started using the Internet in an attempt to improve the efficiency and effectiveness of marketing, provide customers access to information about products and their availability, build brand value, and to offer customers a convenient medium to make purchases online. The most valuable aspects of Internet shopping, as compared to store-based ad catalog shopping, are typically perceived to be competitive pricing, one-source shopping, convenience and time-savings (Corral, 2000). In addition to increasing brand loyalty among consumers, the goals of a manufacturer might include increasing opportunities for collaboration with suppliers and customers. A retailer, in turn, might have goals such as increasing sales or revenue by accepting orders through an Internet storefront, getting more customers to come into traditional bricks-and-mortar stores, and reducing customer service costs by allowing customers to view order-tracking information over the Web. (Machan, 2000) According to Xceed Intelligence, the apparel industry has traditionally been slow to adopt new business practices, and the outdated practices have consequently slowed down adoption of e-commerce (Masters, 2000). Apparel has, nonetheless, become the third-largest retail sales category on the Internet and Forrester Research1 expects online sales of apparel to reach $20.2 billion in 2003 which accounts, however, for just over 7% of total apparel sales. Often heard argument behind the slow take-off lies in the fact that the consumers perceive clothing as products that have to be seen, touched and tried of before the purchase. In addition to the inability to touch, feel or try on clothing on the Internet, consumers have been concerned with returns, security and costs (Kelly, 2000) - worries 1

http://www.forrester.com

1596 ECIS 2002 • June 6–8, Gdańsk, Poland

— First — Previous — Next — Last — Contents —

Computers & Industrial Engineering 50 (2006) 202–219 www.elsevier.com/locate/dsw

Mathematical model and genetic optimization for the job shop scheduling problem in a mixed- and multi-product assembly environment: A case study based on the apparel industry q Z.X. Guo *, W.K. Wong, S.Y.S. Leung, J.T. Fan, S.F. Chan Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong Received 4 September 2005; received in revised form 6 March 2006; accepted 23 March 2006 Available online 11 Juyl 2006

Abstract An effective job shop scheduling (JSS) in the manufacturing industry is helpful to meet the production demand and reduce the production cost, and to improve the ability to compete in the ever increasing volatile market demanding multiple products. In this paper, a universal mathematical model of the JSS problem for apparel assembly process is constructed. The objective of this model is to minimize the total penalties of earliness and tardiness by deciding when to start each order’s production and how to assign the operations to machines (operators). A genetic optimization process is then presented to solve this model, in which a new chromosome representation, a heuristic initialization process and modified crossover and mutation operators are proposed. Three experiments using industrial data are illustrated to evaluate the performance of the proposed method. The experimental results demonstrate the effectiveness of the proposed algorithm to solve the JSS problem in a mixed- and multi-product assembly environment. Ó 2006 Elsevier Ltd. All rights reserved. Keywords: Job shop scheduling; Mathematical model; Optimization; Genetic algorithm; Apparel industry

1. Introduction Today’s enterprises are confronted with ever increasing global competition and unpredictable demand ﬂuctuations. These pressures compel enterprises to continuously improve the performance of their production processes in order to deliver the ﬁnished product within the most approximate period of time and at the lowest production cost. The apparel industry is one which is necessary to operate their assembly systems using mixedand multi-product scheduling method due to rapid market changes.

q *

This manuscript was processed by Area Editor Maged Dessouky. Corresponding author. Tel.: +852 27666465; fax: +852 27731432. E-mail address: zx.guo@polyu.edu.hk (Z.X. Guo).

international journal of

ELSEVIER

production economics Int. J. Production Economics 54 (19981 65 76

Genetic algorithm approach to earliness and tardiness production scheduling and planning problem Y. Li a'*, W.H. Ip a, D.W. W a n g b aDepartment of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong bDepartment o['System Engineering, School o/'lnJormation Science and Engineering, Northeastern University. Shenyang Liaoning, People's Republie of China 110006

Received 8 August 1996; accepted 29 August 1997

Abstract

A genetic algorithm (GA) approach is proposed to address the problem of earliness and tardiness production scheduling and planning (ETPSP) in this paper. The proposed method includes lot-size consideration as well as the conflicting issue of capacity balancing. The common problem of large-scale discrete problem where the restriction of linearity, convexity and differentiability is in the cost function is new one which is completely relaxed by this approach. This paper outlines the fundamental issues of the manufacturing design in a genetic algorithm formulation. Both simulation and comparison results indicate that this new scheduling scheme is an effective and efficient technique to tackle the problem. Keywords: Production scheduling and planning (PSP); Production and inventory management; GA; Manufacturing

resource planning (MRP-II); Just-in-time (JIT)

1. Introduction

MRP-II and JIT are two methods used worldwide for modern production and inventory management. Although they provide many advantages, the high in-process inventory, the nervousness of production planning in M R P - I I manufacturing systems, the shock of bottleneck, sensitiveness of imbalance and uncertainty in JIT manufacturing

*Corresponding author. Tel.: 852-27889956; fax: 852-27887227; e-mail: yli@ee.cityu.edu.hk.

systems (Ho, 1989; Mazzola et al., 1989; Sugimori et al., 1977; Wang and Xu, 1993) are difficult problems and they remain open. To overcome these problems and achieve the best result of production and inventory management, since 1980s (Gunasekaran, 1993; Sarker and Fitzsimmons, 1989) more and more researchers have focused on integrating M R P - I I with JIT philosophy. The presented researches on miscellaneous M R P - I I and JIT mainly focus on the control level of production in manufacturing systems (Hodgson and Wang, 1991a,b). Our research interest focuses on using JIT philosophy to improve the production planning approach of M R P - I I by an efficient

International Journal of Production Research, Vol. 44, No. 21, 1 November 2006, 4465–4490

Determination of fault-tolerant fabric-cutting schedules in a just-in-time apparel manufacturing environment C. K. KWONG*y, P. Y. MOKz and W. K. WONGz

Downloaded By: [Hong Kong Polytechnic University] At: 05:17 26 August 2009

yDepartment of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong zInstitute of Textiles and Clothing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong

(Revision received December 2005) In apparel manufacturing, accurate upstream fabric-cutting planning is crucial for the smoothness of downstream sewing operations. Effective and reliable fabric-cutting schedules are difficult to obtain because the apparel manufacturing environment is fuzzy and dynamic. In this paper, genetic algorithms and fuzzy-set theory are used to generate fault-tolerant fabric-cutting schedules in a just-in-time production environment. The proposed method is demonstrated by two cases with production data collected from a Hong Kong-owned garment production plant in China. Results of the two cases preliminarily show that the genetically improved fault-tolerant schedules effectively satisfy the demand for downstream production units, guarantee consistent and reliable system performance, and also reduce production costs through reduced operator idle time. More cases will be conducted in order to further validate the effectiveness of the proposed method. Keywords: Genetic algorithms; Fuzzy set theory; Parallel machine scheduling; Fabric cutting

1. Introduction Rapid response to customer demand, and a short product development and production lead time are essential for the survival of today’s apparel manufacturers. Advances in information technology and the development of new computation techniques have made real-time apparel design possible. Research on virtual garment simulation to interactive fashion design has received much attention recently (Yang et al. 1992, Cordier et al. 2002, Fuhrmann et al. 2003). In addition to real-time fashion design, new technologies have also been employed for productivity improvement in the apparel-manufacturing process (Wong et al. 2000, Mok et al. in press). Apparel production is a type of assembly manufacturing that involves a number of processes including fabric spreading, cutting, sewing, and finishing. The fabric-cutting operation is done in a cutting department, which usually serves several downstream sewing assembly lines. Ineffective upstream planning causes chaos in the *Corresponding author. Email: mfckkong@polyu.edu.hk International Journal of Production Research ISSN 0020–7543 print/ISSN 1366–588X online ß 2006 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/00207540600597047

Pattern Recognition 32 (1999) 1049—1060

Artificial neural networks for automated quality control of textile seams Claus Bahlmann*, Gunther Heidemann, Helge Ritter AG Neuroinformatik, Technische Fakulta¨ t, Universita¨ t Bielefeld, Universita¨ tsstr. 25, D-33615 Bielefeld, Germany Received 9 January 1998; received in revised form 3 August 1998

Abstract We present a method for an automated quality control of textile seams, which is aimed to establish a standardized quality measure and to lower costs in manufacturing. The system consists of a suitable image acquisition setup, an algorithm for locating the seam, a feature extraction stage and a neural network of the self-organizing map type for feature classification. A procedure to select an optimized feature set carrying the information relevant for classification is described. 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. Keywords: Neural networks; Self-organizing feature maps (SOFM); Textile seams; Quality control; Feature selection

1. Introduction Reliable and accurate quality control is an important element in industrial textile manufacturing. For many textile products, a major quality control requirement is judging seam quality. Presently, this is still accomplished by human experts, which is very time consuming and suffers from variability due to human subjectivity. Consequently, investigations about automated seam quality classification and an implementation of an automated seam classificator are highly desirable. Such a system would be useful not just to objectify quality control of textile articles but it can also provide a basis to perform online adjustment of sewing machine parameters to achieve smoother seams. Previous approaches to automated classification of textile seams were made by Dorrity [1] and Clapp et al.

*Corresponding author. bielefeld.de

E-mail:

icbahlma@techfak.uni-

[2]. Using piezoelectric sensors, Dorrity [1] measures the ratio of thread motion and a sewing machine cycle and compares it to an optimal value. Clapp et al. [2] determine fabric density using beta-rays. From density variation, a quality measure can be derived. However, an optical control method appears to be not only easier to realize from a technical point of view, but also more appropriate, since humans also judge visually. In this contribution we present a system that can judge seam quality from greyvalue images. An overview of the approach is shown in Fig. 1. The first stage is an image acquisition system, which can record the structure of the seams and map it onto a greyvalue image (step ‘‘a’’ in the figure, Section 3). As a next step, an algorithm for locating the seam is applied (b, Section 4). This allows to normalize the position of the acquired image. Next, a set of appropriate features is extracted from the normalized seam images, which have to code information about the quality of the respective seam (c, Section 6). We divide the images into two sets: the first (training set) is used to train

Analytica Chimica Acta 595 (2007) 72–79

Genetic algorithm optimisation combined with partial least squares regression and mutual information variable selection procedures in near-infrared quantitative analysis of cotton–viscose textiles A. Durand, O. Devos, C. Ruckebusch ∗ , J.P. Huvenne Laboratoire de Spectrochimie Infrarouge et Raman (LASIR) UMR CNRS 8516, Bˆat.C5, Universit´e des Sciences et Technologies de Lille (USTL), 59655 Villeneuve d’Ascq, France Received 12 October 2006; received in revised form 2 February 2007; accepted 13 March 2007 Available online 18 March 2007

Abstract In this work, different approaches for variable selection are studied in the context of near-infrared (NIR) multivariate calibration of textile. First, a model-based regression method is proposed. It consists in genetic algorithm optimisation combined with partial least squares regression (GA–PLS). The second approach is a relevance measure of spectral variables based on mutual information (MI), which can be performed independently of any given regression model. As MI makes no assumption on the relationship between X and Y, non-linear methods such as feed-forward artificial neural network (ANN) are thus encouraged for modelling in a prediction context (MI–ANN). GA–PLS and MI–ANN models are developed for NIR quantitative prediction of cotton content in cotton–viscose textile samples. The results are compared to full-spectrum (480 variables) PLS model (FS-PLS). The model requires 11 latent variables and yielded a 3.74% RMS prediction error in the range 0–100%. GA–PLS provides more robust model based on 120 variables and slightly enhanced prediction performance (3.44% RMS error). Considering MI variable selection procedure, great improvement can be obtained as 12 variables only are retained. On the basis of these variables, a 12 inputs ANN model is trained and the corresponding prediction error is 3.43% RMS error. © 2007 Elsevier B.V. All rights reserved. Keywords: Near-Infrared Spectroscopy; Textile; Multivariate calibration; Genetic algorithm; Mutual information; Artificial neural network

1. Introduction Determining the composition of textile is an essential topic due to the wide range of applications in production control, custom check or textile waste sorting and, in the near future, rapid measurement methods such as on-line systems or sensors are expected for this purpose [1–5]. In most applications, the properties of textile samples are derived from the knowledge of the chemical composition. The quantitative measurement of the composition of textiles is thus a critical issue in the industry, more especially as it is framed by the European directive CE 96/74 [6]. The usual analytical methods for textile blends analysis depend on the nature of the considered textiles. In all the cases, the methods are time consuming, not mentioning that harmful chemicals are required for dissolution [7]. On

∗

Corresponding author. Tel.: +33 3 20436661; fax: +33 3 20436755. E-mail address: Cyril.ruckebusch@univ-lille1.fr (C. Ruckebusch).

the contrary, near-infrared (NIR) spectroscopy provides a rapid and direct measurement and, combined with multivariate calibration, it enables accurate determination of textile properties such as raw fibres, finished products or fabrics [8–12]. But a major difficulty in NIR quantitative analysis remains the large sample-to-sample variation in the reflectance spectra of textiles of identical chemical composition. Indeed, spectra are sensitive to many non-chemical factors such as weaving, geographical origin or industrial supplier [8,9,13]. In multivariate calibration, variable selection attempts to identify and remove the variables that penalise the performance of a model since they are useless, noisy and redundant or correlated by chance [14,15]. Variable selection procedures are of particular interest when dealing with spectroscopic data. Indeed, the number of variables is potentially very large with regard to the number of samples at disposal for a regression model. Usually, this dimensionality problem is circumvented using methods such as partial least squares (PLS) regression [16]. But the PLS latent variables calculated may also be affected by redundancies

ARTICLE IN PRESS

Int. J. Production Economics 114 (2008) 615–630 www.elsevier.com/locate/ijpe

Fashion retail forecasting by evolutionary neural networks Kin-Fan Au , Tsan-Ming Choi, Yong Yu Business Division, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Kowloon, Hung Hom, Hong Kong Received 28 August 2006; accepted 15 June 2007 Available online 10 March 2008

Abstract Recent literature on nonlinear models has shown that neural networks are versatile tools for forecasting. However, the search for an ideal network structure is a complex task. Evolutionary computation is a promising global search approach for feature and model selection. In this paper, an evolutionary computation approach is proposed in searching for the ideal network structure for a forecasting system. Two years’ apparel sales data are used in the analysis. The optimized neural networks structure for the forecasting of apparel sales is developed. The performances of the models are compared with the basic fully connected neural networks and the traditional forecasting models. We ﬁnd that the proposed algorithms are useful for fashion retail forecasting, and the performance of it is better than the traditional SARIMA model for products with features of low demand uncertainty and weak seasonal trends. It is applicable for fashion retailers to produce shortterm retail forecasting for apparels, which share these features. r 2008 Elsevier B.V. All rights reserved. Keywords: Forecasting; Evolutionary neural networks; SARIMA

1. Introduction In fashion retailing, demand uncertainty is notorious of creating many big challenges in logistics management (Hammond, 1990). Following the fashion trend and market response, fashion products have a highly unpredictable demand. In order to avoid stock-out and maintain a high inventory ﬁll rate, fashion retailers need to keep a substantial amount of safety stock. In order to reduce the inventory burden, fashion retailers have adopted various measures such as the accurate response policy (Fisher and Raman, 1996) and Corresponding author. Tel: +852 2766 6428; fax: +852 2773 1432. E-mail address: tckfau@inet.polyu.edu.hk (K.-F Au).

quick response policy (Iyer and Bergen, 1997; Au and Chan, 2002; Choi et al., 2006; Choi and Chow, 2007). Some fashion retailers improve their decisions by acquiring market information and revising their forecast in multiple stages (see Donohue, 2000; Gallego and Ozer, 2001; Sethi et al., 2001; Choi et al., 2003, 2004; Tang et al., 2004; Choi, 2007). By utilizing market information (e.g., the sales of other closely related fashion products), fashion retailers can reduce the forecast error and it is widely believed that it can help to reduce inventory cost, and hence improve proﬁt (e.g., see Eppen and Iyer, 1997). Undoubtedly, forecasting is one crucial task in retail supply chains (Luxhoj et al., 1996; Chu and Zhang, 2003; Thomassey et al., 2005; Sun et al., 2007) and it can affect the retailer and other channel members. We hence propose to investigate in this

Pattern Recognition 36 (2003) 1645 – 1659

www.elsevier.com/locate/patcog

Neural network based detection of local textile defects Ajay Kumar∗ Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong Received 5 April 2002; accepted 28 October 2002

Abstract A new approach for the segmentation of local textile defects using feed-forward neural network is presented. Every fabric defect alters the gray-level arrangement of neighboring pixels, and this change is used to segment the defects. The feature vector for every pixel is extracted from the gray-level arrangement of its neighboring pixels. Principal component analysis using singular value decomposition is used to reduce the dimension of feature vectors. Experimental results using this approach illustrate a high degree of robustness for the detection of a variety of fabric defects. The acceptance of a visual inspection system depends on economical aspects as well. Therefore, a new low-cost solution for the fast web inspection using linear neural network is also presented. The experimental results obtained from the real fabric defects, for the two approaches proposed in this paper, have con2rmed their usefulness. ? 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. Keywords: Defect detection; Machine vision; Automated visual inspection; Quality assurance; Neural networks

1. Introduction Automated visual inspection of industrial goods for quality control plays an ever-increasing role in production process as the global market pressures put higher and higher demand on quality. In most cases, the quality inspection through visual inspection is still carried out by humans. However, the reliability of manual inspection is limited by ensuing fatigue and inattentiveness. For example in textile industry, the most highly trained inspectors can only detect about 70% of the defects [1]. Therefore, the automation of visual inspection process is required to maintain high quality of products at high-speed production. Some of the most challenging visual inspection problems deal with the textured materials. Three common criterion used to measure the quality index of textured materials are related to material isotropy, homogeneity and texture coarseness [2]. While there is a remarkable similarity in the overall automation requirements for the textured materials, the cost eAective solutions are problem speci2c and require ∗

Tel.: +852-23-58-8384; fax: +852-23-58-1477. E-mail address: ajaykr@cs.ust.hk (A. Kumar).

extensive research and development eAorts. Quality assurance in production lines for textured materials such as wood [3], steel-roll [4], paper [5], carpet [6], textile [1,7–21], etc., have been studied by various researchers. The detection of local fabric defects is one of the most intriguing problems in visual inspection, and has received much of the attention over the years [7–21]. This paper focuses on this problem and investigates some new techniques to address this problem. 1.1. Prior work Fabric defect detection using digital inspection images has received considerable attention during the past decade and numerous approaches have been proposed in the literature [7–21]. At microscopic level, the inspection problems encountered in digital images become texture analysis problems. Therefore texture features based on statistical, geometrical, structural, model based, or signal-processing approaches are the potential source of investigation [22]. In the approach by Cohen et al. [7] Gauss Markov Random Field (GMRF) model has been used for the characterization of fabric textures. The web inspection problem is

Dyes and Pigments 42 (1999) 123±135

The use of arti®cial neural network (ANN) for modeling of the H2O2/UV decoloration process: part I Yness March Slokar a,*, Jure Zupan b, Alenka Majcen Le Marechal a a

Faculty for Mechanical Engineering, Smetanova 17, 2000 Maribor, Slovenia b National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia Received 5 November 1998; accepted 18 January 1999

Abstract A brief introduction into arti®cial neural networks (ANNs) is given, with emphasis on counter-propagation learning strategy, as well as their use for the purpose of modeling and optimization of H2O2/UV decoloration process. The use of Plackett±Burman partial factorial design for seven variables on three di erent levels, for the selection of experiments, needed to calculate the signi®cance of variables, is described. Results of learning with Kohonen ANN are described, and the best prediction assembly suggested. # 1999 Elsevier Science Ltd. All rights reserved. Keywords: Arti®cial neural network; Decoloration; Variables; Partial factorial design; Ecological parameters; Modeling

1. Introduction E uents of dye manufacturers and dye user companies are usually highly polluted with di erent types of dyes. Even though there are not many dyes that are proven to be carcinogenic for humans, the extent of dyes in surface waters is rising, and methods for their removal need to be evaluated. Due to the large number of di erent dyes that are available on the market at present (over 3000) [1], it is almost impossible to ®nd a perfect method which would satisfactorily purify the waste-waters, regardless of the chemical nature of the pollutant. Many possibilities have been reported [2], but these are more or less selective. Even for the same dye, the decoloration process may depend on many di erent factors. For a * Corresponding author c/o Tiziana D'Adda, Strada D'Azeglio 55, 43100 Parma, Italy.

decoloration process, one usually has to consider the time needed for the decoloration to be completed, i.e. the time needed for the dye to be either completely removed or to be removed up to a reasonable amount. It is desired that this time is as short as possible. For these reasons our research group wanted to optimize the decoloration process, which implies that a model to predict the time needed for decoloration to conclude had to be obtained. Modeling of the decoloration process involves many problems, since the process depends on many factors, i.e. we are dealing with a multivariate system. Furthermore, the concentration of the dye is not the only parameter of interest; ecological parameters such as COD, BOD and TOC, are also important. This means our system is also a multi-response one. It is also evident that these problems cannot be solved by simple linear multivariate correlation [3]. Recently, arti®cial neural

Engineering the drapability of textile fabrics George K Stylios; Norman J Powell International Journal of Clothing Science and Technology; 2003; 15, 3/4; ABI/INFORM Global pg. 211

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

Available online at www.sciencedirect.com

Computers & Industrial Engineering 54 (2008) 889–902 www.elsevier.com/locate/caie

Multiple-objective genetic optimization of the spatial design for packing and distribution carton boxes S.Y.S. Leung, W.K. Wong, P.Y. Mok

Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong Received 24 July 2006; received in revised form 20 July 2007; accepted 29 October 2007 Available online 6 November 2007

Abstract Packing and cutting problems, which dealt with filling up a space of known dimension with small pieces, have been an attractive research topic to both industry and academia. Comparatively, the number of reported studies is smaller for container spatial design, i.e., defining the optimal container dimension for packing small pieces of goods with known sizes so that the container space utilization is maximized. This paper aims at searching an optimal set of carton boxes for a towel manufacturer so as to lower the overall future distribution costs by improving the carton space utilization and reducing the number of carton types required. A multi-objective genetic algorithm (MOGA) is used to search the optimal design of carton boxes for a one-week sales forecast and a 53-week sales forecast. Clustering techniques are then used to study the order pattern of towel products in order to validate the genetically generated results. The results demonstrate that MOGA effectively search the best carton box spatial design to reduce unfilled space as well as the number of required carton types. It is important to note that the proposed methodology for optimal container design is not limited to the apparel industry but practically attractive and applicable to every industry which aims for distribution costs reduction. 2007 Elsevier Ltd. All rights reserved. Keywords: Multi-objective genetic algorithms; Clustering technique; Packing and cutting; Container design

1. Introduction Packing and cutting problem is an active topic of research and has numerous applications in many industries. For example, electronic components are packed into a minimal case to form a device in electronic industry. In the case of manufacturing industry, a large sheet of fabric, glass, paper, or woods is usually cut into several smaller pieces of known dimensions. For another instance, loading pallets with goods or ﬁlling containers with cargo in distribution industry (Pisinger, 2002). The research on packing and cutting was originated from the seminal work of Gilmore and Gomory (1965). Packing and cutting problem appears in many related studies such as knapsack loading, assortment, pallet loading, and container loading. In the knapsack loading of a container, each box has an associated proﬁt and the problem is to choose a subset of the *

Corresponding author. Tel.: +852 2766 4442; fax: +852 2773 1432. E-mail address: tracy.mok@inet.polyu.edu.hk (P.Y. Mok).

Journal of Materials Processing Technology 140 (2003) 95–99

Constructing an intelligent conceptual design system using genetic algorithm Jeng-Jong Lin Department of Textile Science, Van Nung Institute of Technology, Chung-Li, Tao-Yuan, Taiwan, ROC

Abstract Product design is a problem of multi-solution and how to find out the design schema, which is fit for the target demand, is a challenge for a designer to deal with at any moment. The main goal of this paper is focused on developing an intelligent conceptual design system, through whose assistance, the crucial problem for a designer mentioned above can thus be eliminated. © 2003 Published by Elsevier B.V. Keywords: Conceptual design; Genetic algorithm; Search mechanism; Evaluation mechanism

1. Introduction The evaluation of an object is through closely comparing it with the others, a specific value of the object to someone can thus be defined. In other words, the value of an object to someone can be obtained by evaluation. The evaluation way is set as a forward inductive inference [1], which is of a “1 to 1” mathematic relationship. In other word, the value for specific object to a certain individual is unique as only one kind of value. It never happens there exist two or more options for the value identification. For an instance, judging from the appearance, the value of a car, which is manufactured by assembly from various mechanical components, is always deemed as only one specific value. This kind of inference is so called a “forward inductive inference of evaluation” (i.e., A → B), which is illustrated as Fig. 1a. On the contrary, while proceeding with reverse inference based on the same viewpoint, we can recognize why a certain kind of product style is so as to be represented. This kind of reverse inference is a way of “knowledge”. While proceeding with product design, firstly, a certain value level will be set as target and then start to manufacture the product, which can live up to the previously set target value, by assembly from a few of appropriate components. It depends on the “forward inductive inference of knowledge” [1], i.e., the backward deductive inference of “evaluation”, which is of a “1 to n” mathematic relationship. In other words, there exist many products, which are of the identical value that can live up to the customer’s demand. There will be several different combinations of components to create different products to meet the desired target value. For example, a consumer is searching for a preferred car, which is of certain 0924-0136/$ – see front matter © 2003 Published by Elsevier B.V. doi:10.1016/S0924-0136(03)00691-5

kind of his expected value. There must exist many different combinations for components to assembly as various cars, all of which can meet the customer’s expected value. The way to find different assembly combinations for a product, which can meet the expected demand, depends on forward inductive inference of knowledge, i.e., backward deductive inference of evaluation (i.e., B → A), which is illustrated as Fig. 1b. Product design is a reverse inference by evaluation [1–5] (i.e., Y → X: a forward inference by knowledge) shown as Fig. 1d. How to find the attributes combinations to live up to customer’s conceptual demand is the very problem for a designer to get solved. In this paper, we present an intelligent conceptual design system (ICDS), which is developed using genetic algorithm (GA) to construct a search mechanism to find various solutions to the product design constrains. The ICDS can fulfill a computer with intelligence to create the design inspiration for a designer. The goal of ICDS is to provide a function for the support and explicit capture of the top-down apparel design process.

2. Design by backward deductive inference General speaking, the procedure of forming viewpoint on events for mankind (i.e., A → B) is firstly by judging the general attributes from a specific object, then the relationship between physical mechanics of architecture and psychology concept of image for the object can thus be created. On the contrary, despite of proceeding with the judgment directly, the evaluation can be approached through setting up the relationship between various combinations of the attributes of various design factors. It is no longer need the

ARTICLE IN PRESS

Robotics and Computer-Integrated Manufacturing 22 (2006) 279–287 www.elsevier.com/locate/rcim

Genetic algorithms for the optimal common due date assignment and the optimal scheduling policy in parallel machine earliness/tardiness scheduling problems Liu Min , Wu Cheng Department of Automation, Tsinghua University, Beijing 100084, China Received 23 November 2002; accepted 23 December 2004

Abstract Earliness/tardiness scheduling problems with undetermined common due date which have wide application background in textile industry, mechanical industry, electronic industry and so on, are very important in the research fields such as industry engineering and CIMS. In this paper, a kind of genetic algorithm based on sectional code for minimizing the total cost of assignment of due date, earliness and tardiness in this kind of scheduling problem is proposed to determine the optimal common due date and the optimal scheduling policy for determining the job number and their processing order on each machine. Also, simulated annealing mechanism and the iterative heuristic fine-tuning operator are introduced into the genetic algorithm so as to construct three kinds of hybrid genetic algorithms with good performance. Numerical computational results focusing on the identical parallel machine scheduling problem and the general parallel machine scheduling problem shows that these algorithms outperform heuristic procedures, and fit for larger scale parallel machine earliness/tardiness scheduling problem. Moreover, with practical application data from one of the largest cotton colored weaving enterprises in China, numerical computational results show that these genetic algorithms are effective and robust, and that especially the performance of the hybrid genetic algorithm based on simulated annealing and the iterative heuristic fine-tuning operator is the best among them. r 2005 Elsevier Ltd. All rights reserved. Keywords: Parallel machine; Genetic algorithm; Heuristic fine-tuning operator; Simulated annealing; Scheduling; Textile

1. Introduction With the success of just-in-time (JIT) production mode in Japan, earliness/ tardiness scheduling problems with the purpose of JIT production have become an active research ﬁeld. In 1990, Baker and Scudder [1] presented the ﬁrst survey on earliness/tardiness scheduling problems, then some papers [2–10] related to this kind of scheduling problems were published. Currently, most works proposed mainly focus on single machine earliness/tardiness scheduling problems and parallel machine earliness/tardiness scheduling problems with Corresponding author. Tel.: +86 10 62793756.

ﬁxed due date constraint, but the parallel machine earliness/tardiness scheduling problems with undetermined common due date constraint are very typical in industrial production practice and have wide application background in textile industry, mechanical industry, electronic industry and so on, in which both the optimal common due-date and the optimal scheduling policy used to determine the job number and their processing order on each machine need determining at the same time. For the earliness/tardiness scheduling problem with common due-date constraint, only a few works [4,9,11–14] were proposed. Aiming at single machine problem, Cheng [11] proposed the method for determining the optimal job sequence, and the result obtained was generalized to the identical parallel machine

Engineering Applications of Artificial Intelligence 13 (2000) 635–644

Application of interactive genetic algorithm to fashion design Hee-Su Kim, Sung-Bae Cho* Department of Computer Science, Yonsei University, 134 Shinchon-dong, Sudaemoon-ku, Seoul 120-749, South Korea

Abstract In general, computer-aided design support systems have got an approach of traditional artificial intelligence, which statistically analyzes data such as the behavior of designer, to extract formal design behavior. This approach, however, can neither deal with continuous change of fashion nor reflect personal taste well, as it just depends on large amount of collected data. To overcome this sort of problem interactive genetic algorithm (IGA) has been recently proposed, as a new trend of evolutionary computation. IGA uses human’s response as fitness value when the fitness function cannot be explicitly defined. This enables IGA to be applied to artistic domains, and we propose a fashion design aid system using it. Unlike the previous works that attempt to model the dress design by several spline curves, the proposed system is based on a new encoding scheme that practically describes a dress with three parts: body and neck, sleeve, and skirt. By incorporating the domain-specific knowledge into the genotype, we could develop a more realistic design aid system for women’s dress. We have implemented the system with OpenGL and VRML to enhance the system interface. The experiments with several human subjects show that the IGA approach to dress design aid system is promising. # 2000 Elsevier Science Ltd. All rights reserved. Keywords: Interactive genetic algorithm; Fashion design; Subjective evaluation; Domain knowledge; User satisfaction; OpenGL

1. Introduction One of the biggest changes since the industrial revolution is on the market economy. Think about clothes market. Before the Industrial Revolution, consumers had to make their own clothes or buy one from very small producers. Naturally they have few choices on it. However, the Industrial Revolution enables massproduction, and now consumers can make their choice from very large amount of clothes. The trend that consumers lead the market is now on progress. Perhaps in the future, consumers can order their favorite design to the manufacturer, and then a cloth is produced according to that design (Brockman, 1965). As most consumers are not professional at design, however, a sophisticated computer-aided design system might be helpful to choose and order what they want. It can be a solution that designer contacts consumers and percepts their favorite design, but it is not efficient in terms of cost and time. Computer-aided fashion design systems for non-professional may search out user’s preferential design efficiently. *Corresponding author. Tel.: +82-2-361-2720; fax: +82-2-3652579. E-mail address: sbcho@csai.yonsei.ac.kr (S.-B. Cho).

In this paper we develop a fashion design aid system with interactive genetic algorithm (IGA) using domainspecific knowledge. We have classified women’s dress design into 3 parts, made them as separate 3-D models with OpenGL and GLUT library, and produced individual designs from combination of these models. Through the interaction with user, our system can effectively suggest the nearest design of what the user prefers to. This paper is organized as follows: Section 2 introduces fashion design and conventional fashion design aid systems, and gives an account of IGA. Section 3 describes the overview of the system, genotype encoding, and genetic operators. Section 4 gives 3-D modeling process and system implementation using OpenGL and GLUT library. Section 5 analyzes some experimental results.

2. Background 2.1. Fashion design The word ‘design’ originated from ‘designare’ of the Latin language, which means ‘to symbolize some plan’.

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Application of artificial neural networks to the prediction of sewing performance of fabrics Patrick C.L. Hui Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

Application of artificial neural networks 291 Received 12 January 2006 Accepted 5 February 2007

Keith C.C. Chan Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong, and

K.W. Yeung and Frency S.F. Ng Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Kowloon, Hong Kong Abstract Purpose – This paper aims to investigate the use of artificial neural networks (ANN) to predict the sewing performance of fabrics. The purpose of this study is to verify the ANN techniques that could be emulated as human decision in the prediction of sewing performance of fabrics. Design/methodology/approach – In order to verify the ANN techniques that could be emulated as human decision in the prediction of sewing performance of fabrics, 109 data sets of fabrics were tested by using fabric assurance by simple testing system and the sewing performance of each fabric’s specimen was assessed by the domain experts. Of these 109 input-output data pairs, 94 were used to train the proposed backpropagation (BP) neural network for the prediction of the unknown sewing performance of a given fabric, and 15 were used to test the proposed BP neural network. Findings – After 10,000 iterations of training of BP neural network, the neural network converged to the minimum error level. The experimental results reveal the great potential of the proposed approach in predicting the sewing performance of fabrics for apparel production. Originality/value – Generally, the fabric’s performance in the manufacturing process is judged subjectively by the operators and/or their supervisors. Current methodologies of acquiring fabric property information and predicting fabric sewing performance are still incapable of providing a means for efficient planning and control for the sewing operation. Further, development of techniques to predict the sewing performance of fabric is essential for the current apparel production environment. In this paper, the use of ANN to predict the sewing performance of fabrics in garment manufacturing is investigated. Keywords Neural nets, Fabric testing Paper type Research paper

In apparel production, the sewing process is one of the critical processes in the determination of productivity and the quality of the finished garment. Consistency of sewing quality is essential if the apparel manufacturer is to satisfy the customer. Failings or variations in sewing quality may be caused by one or both of the following factors: mechanical factor (the sewing machine) and human factor (the operator).

International Journal of Clothing Science and Technology Vol. 19 No. 5, 2007 pp. 291-318 q Emerald Group Publishing Limited 0955-6222 DOI 10.1108/09556220710819500

Information Sciences 123 (2000) 181±199

www.elsevier.com/locate/ins

An intelligent approach to integration and control of textile processes Sungshin Kim a, George J. Vachtsevanos a

b,*

Department of Electrical Engineering, Pusan National University, Pusan, South Korea b School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250, USA

Received 20 September 1998; received in revised form 15 August 1999; accepted 29 October 1999

Abstract This paper introduces a methodology to integrate and control e ectively major plant processes with strong couplings between them. The proposed integration philosophy consists of cause±e ect relationships and decides upon control setpoints for the individual processes by optimizing a global objective function which aims at improving process yield. A neuro-fuzzy model and a fuzzy objective function are employed to address the integration and control tasks. Such models and objective functions are de®ned and developed using experimental data or an operator's experience. The objective is to maximize productivity and at the same time, reduce defects in each of the subsequent operations. A textile plant is considered as a testbed and three major processes ± warping, slashing and weaving ± are employed to illustrate the feasibility of the approach. The supervisory level of the control architecture is intended to continuously improve the control setpoints depending upon feedback information from the weave room, slasher operator, and warping data. Ó 2000 Published by Elsevier Science Inc. All rights reserved. Keywords: Polynomial fuzzy neural networks; Fuzzy logic control; Integration; Cause± e ect relation; Genetic algorithms; Hybrid genetic optimization

Corresponding author. E-mail addresses: sskim0@hyowon.pusan.ac.kr (S. Kim), gjv@ee.gatech.edu (G.J. Vachtsevanos). 0020-0255/00/$ - see front matter Ó 2000 Published by Elsevier Science Inc. All rights reserved. PII: S 0 0 2 0 - 0 2 5 5 ( 9 9 ) 0 0 1 3 0 - 9

An integrated machine vision based system for solving the nonconvex cutting s... Sam Anand; Christopher McCord; Rohit Sharma; Thiagarajan Balachander Journal of Manufacturing Systems; 1999; 18, 6; ABI/INFORM Global pg. 396

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Engineering Applications of Arti®cial Intelligence 13 (2000) 381±390

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An implementation of genetic algorithms for rule based machine learning S. Sette a,*, L. Boullart b a

Department of Textiles, University of Ghent, Technologiepark 9, 9052 Zwijnaarde, Belgium Department of Automation & Control Engineering, University of Ghent, Technologiepark 9, 9052 Zwijnaarde, Belgium

Received 1 July 1999

Abstract Genetic algorithms have given rise to two new ®elds of research where (global) optimisation is of crucial importance: `Genetic Programming' and `Genetic based Machine Learning' (GBML). In this paper the second domain (GBML) will be introduced. An overview of one of the ®rst GBML implementations by Holland, also known as the Learning Classi®er Systems (LCS) will be given. After describing and solving a well-known basic (educational) problem a more complex application of GBML is presented. The goal of this application is the automatic development of a rule set for an industrial production process. To this end, the case study on generating a rule set for predicting the spinnability in the ®bre-to-yarn production process will be presented. A largely modi®ed LCS, called Fuzzy E ciency based Classi®er System (FECS), originally designed by one of the authors, is used to solve this problem successfully. 7 2000 Elsevier Science Ltd. All rights reserved. Keywords: Genetic based machine learning; Learning classi®er systems; Fuzzy e ciency based classi®er systems; Textiles; Production process

1. Introduction Sette et al. (1996) set forth the basic principles of Genetic Algorithms and some accompanying techniques (sharing function, Pareto optimisation, etc.) which were applied in an industial production process. The basic representation scheme of the optimising parameter thereby was a simple (mostly 1 byte) string. This `chromosome'-string was subject to the genetic (Darwinist) manipulation, leading from one population to the next towards ever more ®t o springs surpassing their parents. For many problems this representation scheme is really a key issue, since it directly re¯ects its fundamental behaviour. A ®xed string may in practice limit not only the class of suitable problems, but also the optimising capabilities in the problem itself. * Corresponding author. Tel.: +32-9264-5744; fax: +32-92645846. E-mail addresses: stefan.sette@rug.ac.be (S. Sette), boullart@autoctrl.rug.ac.be (L. Boullart).

Among the limitations are: no hierarchical structures, no recursiveness, lack of computational procedures, no variability on the string length, no or poor self-adaptive mechanisms. Sometimes this can be solved by `smart' encoding techniques, but in many cases there is no outcome. Therefore, there is certainly need for more complexity in the structures undergoing adaptation by genetic mechanisms. There are two important ways to escape from this rigid ®xed string representation scheme. The ®rst mechanism is the so-called ``classi®er systems'', which is a cognitive architecture in which the genetic algorithms allow adaptive modi®cations of a population of string based if±then rules: i.e., an architecture with a self learning if±then rule based system, where learning is based on some economical reward/ punish principle and genetic algorithms are used to inject `genetic' material. Those classi®ers are described in the underlying paper. The second mechanism is the so-called ``genetic programing'', where computer programs are self generated by the genetic paradigm to perform a speci®c task or

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Pergamon

AEEF: A Knowledge-Based Framework for Apparel Enterprise Evaluation SAMBASIVAN NARAYANAN,* L. H O W A R D OLSON, AND SUNDARESAN JAYARAMAN Georgia Institute of Technology,Atlanta, GA

Abstract-- The practice of subcontracting some or all the operations involved in manufacturing products

is prevalent in many industries. The buying organization typically receives bids from several companies offering to carry out these operations. The process of determining whether a manufacturing facility is capable of producing the required quantity of the commodity at the right time and of the specified quality isfairly complex and involved. In this research, a knowledge-based approach has been adopted to identify the major factors that affect the capability of an apparel manufacturing enterprise to perform on a contract. This knowledge-based framework, known as Apparel Enterprise Evaluation Framework ( AEEF), has been developed. In this article, we present the acquisition and representation of the knowledge in a structured hierarchical framework. The article also outlines the selection of the inference mechanism and the decomposition of the high-level abstract enterprise capabilities into lowlevel observable factors.

1. I N T R O D U C T I O N

ability of the bidder to fulfill the performance requirements of the contract. Selecting the lowest bidder may appear to be beneficial at the time of awarding the contract, but it may not necessarily turn out to be the overall best value decision. This is because the total cost involved in the specific lowest bid contract may be higher than the initial bid, as a result of poor quality or failure to fulfill the customer's order on time. So the evaluation of the technological competence of the bidders becomes essential in deciding which bidder should be awarded the contract, and knowledge of the bidders' manufacturing and other capabilities is a prerequisite for performing this evaluation. In this article, the need for the research, the basic research methodology, the knowledge acquisition for the evaluation framework, and the representation of the acquired knowledge in a structured framework are discussed.

The practice of subcontracting some or all the operations involved in manufacturing products is prevalent in many industries. The buying organization typically receives bids from several companies offering to carry out these operations. To obtain a good quality product at the right time the buying organization must ensure the bidder's capability to manufacture the product to its requirements. Therefore, there is a need to evaluate the facilities of the bidder's enterprise. The process of determining whether a manufacturing facility is capable of producing the required quantity of the commodity at the right time and of the specified quality is fairly complex and involved. Also, when more than one contractor bids for manufacturing a product, the buyer needs to evaluate a specific manufacturing facility in comparison to others. The process of selecting the bidder likely to deliver the best value for the incurred cost is known as source selection. Normally, this process is carried out by experts in the area, and they evaluate bidders according to several criteria, such as manufacturing capability, quality capability, and financial capability. The ultimate objective of any procurement process is to get the best overall value for the buyer, which is a trade-offbetween the price quoted in the bid and the

2. C O M P U T E R - I N T E G R A T E D M A N U F A C I ' U R I N G (CIM) Computer-Integrated Manufacturing (CIM) is the philosophy of manufacturing that concentrates on automation of various activities in a manufacturing enterprise with special emphasis on coordination between those activities to achieve integration. CIM involves integrating computers in various functions of an enterprise to produce the fight product at the right time, of the right quality and at the right price (Jayaraman, 1990). Therefore, an important prerequisite for the implementation of CIM is the in-depth knowledge

* Present address: CAPS LOGISTICSlnc, 2700, Cumberland Parkway Ste. 150,Atlanta, GA, 30339-3321. Requests for reprints should be sent to Sundaresan Jayararnan, Georgia Institute of Technology,Atlanta, GA 30332-0295. 337

Dyes and Pigments 68 (2006) 89e94 www.elsevier.com/locate/dyepig

Achieving expected depth of shade in reactive dye application using artiﬁcial neural network technique M. Senthilkumar, N. Selvakumar* Department of Textile Technology, A.C. College of Technology, Anna University, Chennai 600025, India Received 25 May 2004; received in revised form 13 August 2004; accepted 20 December 2004 Available online 7 March 2005

Abstract Achieving the expected depth of shade in the production of dyed goods is a very important aspect. It requires the termination of the process at the right time in other words, correct duration of dyeing should be used. Prediction of this duration for the application of reactive HE dyes on cotton fabric using artificial neural network (ANN) is reported. The results obtained from the network gives an average training error of around 1% in the prediction of the time duration for achieving the correct depth of shade. The trained network gives the same average error % when tested with other reactive HE dyes even when the input parameters selected are beyond the range of inputs, which were used for training the network. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Artificial neural network; Neuron; Sigmoid function; Hidden layers; Total dye fixed; Spectral reflectance curve

1. Introduction Expected depth of shade is one of the very important qualities to be achieved in the dyed goods. In case, the depth produced is different from that of the expected, the product has to be either taken for reworking or rejected. When goods are taken for dyeing, once the recipe and the conditions of dyeing for a given machine is fixed, the only parameter which needs attention to achieve the expected depth of shade is ‘‘the duration of the process’’. The required dyeing duration for a given situation can be predicted using statistical tools such as multiple regression analysis or computational processors such as artificial neural networks (ANN). Prediction using ANNs is claimed to have better accuracy compared to multiple regression analysis [1,2].

* Corresponding author. Tel.: C91 44 22203564. E-mail address: nselva@annauniv.edu (N. Selvakumar). 0143-7208/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.dyepig.2004.12.016

Neural networks are used for modelling non-linear problems and to predict the output values for a given input parameters from their training values. Most of the textile processes and the related quality assessments are non-linear in nature and hence neural networks ﬁnd application in textile technology. Web density control in carding [3], prediction of yarn strength [4], ring and rotor yarn hairiness [5], total hand evaluation of knitted fabrics [6], classiﬁcation of fabric [7] and dyeing [8] defects, tensile properties of needle punched non-wovens [2], quality assessment of carpets [9], dye concentrations in multiple dye mixtures [1], modelling of the H2O2/UV decolouration process [10], automated quality control of textile seams [11], fabric processability in garment making [12] and evaluation of seam puckering in garments [13] are some of the areas where ANNs have been attempted. An attempt made on the prediction of dyeing time required to achieve expected depth of shade in the application of reactive HE dyes on cotton fabric using ANN is reported in this paper.

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IJCST 12,1

50 Received January 1998 Revised September 1999 Accepted September 1999

A study of the roll planning of fabric spreading using genetic algorithms C.L. Hui Patrick and S.F. Ng Frency

Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong, ROC, and

C.C. Chan Keith

Department of Computing, The Hong Kong Polytechnic University, Hong Kong, ROC Keywords Fabric, Garments, Manufacturing Abstract In the process of fabric spreading, the variance of fabric yardage between fabric rolls may lead to a difference in fabric loss during spreading. As there are numerous combinations the arrangement of the fabric roll sequences for each cutting lay, it is difficult to construct a roll planning to minimise the fabric wastage during spreading in apparel manufacturing. Recent advances in computing technology, especially in the area of computational intelligence, can be used to handle this problem. Among the different computational intelligence techniques, genetic algorithms (GA) are particularly suitable. GAs are probabilistic search methods that employ a search technique based on ideas from natural genetics and evolutionary principles. This paper presents the details of GA and explains how the problem of roll planning can be formulated for GA to solve. The result of the study shows that an optimal roll planning can be worked out by using GA approach. It is possible to save a considerable amount of fabric when the best roll planning is used for the production.

International Journal of Clothing Science and Technology, Vol. 12 No. 1, 2000, pp. 50-62. # MCB University Press, 0955-6222

Introduction In clothing production, the fabric cost alone is about 35-40 percent of the selling price of a garment, that is the major cost item in clothing product[1]. In recent years, the price of fabric has increased continuously, so a certain percent reduction in fabric cost would affect the total manufacturing cost. The fabric spreading and cutting is the major production process that determines the material utilization as well as the finished quality of the garment. Apart from the fabric loss due to the fabric flaws, there are two causes of fabric loss in the production process: (1) marking loss or marker fallout, which is formed because of the gaps and other non-usable areas that take place between the garment panels of a marker; and (2) spreading loss, which is the fabric loss that exists during the spreading process other than the loss caused by the marker arrangement; these include the end loss, width loss, splicing loss and remnant loss. We are grateful to Mr Lewis Chung for preparing the coding and the results of this experiment in graphic form.

European Journal of Operational Research 161 (2005) 275–284 www.elsevier.com/locate/dsw

Stochastics and Statistics

A short and mean-term automatic forecasting system––application to textile logistics S ebastien Thomassey *, Michel Happiette, Jean Marie Castelain Laboratoire GEMTEX-ENSAIT, 9 rue de lÕErmitage, 59100 Roubaix, France Received 25 July 2002; accepted 3 September 2002 Available online 13 December 2003

Abstract In order to reduce their stocks and to limit stock out, textile companies require specific and accurate sale forecasting systems. More especially, textile distribution involves different forecast lead times: mean-term (one year) and short-term (one week in average). This paper presents two new complementary forecasting models, appropriate to textile market requirements. The first model (AHFCCX) allows to automatically obtain mean-term forecasting by using fuzzy techniques to quantify influence of explanatory variables. The second one (SAMANFIS), based on a neuro-fuzzy method, performs short-term forecasting by readjusting mean-term model forecasts from load real sales. To evaluate forecasts accuracy, our models and classical ones are compared to 322 real items sales series of an important ready to wear distributor. 2003 Elsevier B.V. All rights reserved. Keywords: Forecasting; Fuzzy inference system; Neuro-fuzzy model; Textile logistics

1. Introduction Textile managers must use forecasting systems, in order to set up all logistical steps required to produce and deal with a product. The eﬃciency of the supply management optimization relies on the forecast accuracy of the ﬁnished product sales (Sboui et al., 2001; Graves et al., 1998). Sales forecasting in textile industry is very complex. Indeed, a wide range of textile item references exists (about 15 000 per year), and their

Corresponding author. E-mail address: sebastien.thomassey@ensait.fr (S. Thomassey).

historic sale data are often short (104 periods: 2 years on 52 weeks) and particularly perturbed by numerous factors, which are neither strictly controlled nor identified (De Toni, 2000). These factors can depend on the item (colors, price,. . .), distributor (number of stores, merchandizing,. . .), customers (fashion,. . .) or external factors (weather, holidays,. . .). These data are not always available and have different influences on sales (Vroman, 2000). The various stage durations of a textile items development (Fig. 1) implies the need for prediction up to one year before the raw materials are ordered. Production managers also require item quantities to manufacture, particularly early in the case of imported items from far away countries. It

Computers in Industry 57 (2006) 82–92 www.elsevier.com/locate/compind

A new fuzzy approach to improve fashion product development T.W. Lau a, Patrick C.L. Hui a,*, Frency S.F. Ng a, Keith C.C. Chan b a

Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China b Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China Received 1 June 2004; accepted 28 April 2005 Available online 26 September 2005

Abstract This study attempts to use a fuzzy expert system with gradient descent optimization for prediction of fabric specimens in fashion product development. Compared with the traditional methods used fabric mechanical properties to predict fabric specimens, our advisory system accepts fabric hand descriptors which are more closely related to the sensory judgments made by individuals during fabric selection. Fifty participants were selected to evaluate the performance of the proposed fuzzy fabric advisory system. They were asked to express their preferred fabric specimen on inputs of the 14 bipolar fabric hand descriptors in the system. The fuzzy prediction rules associated with the membership functions of each fabric specimen were developed from a survey. After fine-tuning of the proposed system, the prediction accuracy is over eighty percent. The outcomes of this study could help consumers to select the most appropriate fabric and provide field practitioners appropriate suggestions for effective product development in clothing and fashion industries. # 2005 Elsevier B.V. All rights reserved. Keywords: Fuzzy system; Sensory knowledge; Fabric specimen prediction; Fabric hand descriptors

1. Introduction Traditionally, field practitioners rely on their knowledge and experience to select an appropriate fabric material for product development. Automation of the fabric selection becomes an interesting research area for clothing industries. Abbott [1] and Howorth and Oliver [6] in 1950 and 1958 were the pioneers to look at the mechanical properties of fabric closely related to fabric hand. They extracted those key properties by using the multiple factor analysis. Sudnik [18] in 1972 applied the laws in perceptual psychophysics, the Weber-Fechner’s law and the Steven’s power law in specific, for the selection of fabric mechanical properties related to fabric hand. The first prediction model on fabric hand was proposed in 1980 by Kawabata [9] who designed a linear regression model to predict a total hand value (THV) which is a coarse attribute to grade fabric hand. Pan et al. [13] in 1988 reformulated the linear regression model to the nonlinear model. The nonlinear mapping of fabric mechanical properties to fabric hand was further extended to intelligent systems. However, those previous models for predicting the fabric hand are inefficient. The THV derived from the fabric * Corresponding author. Tel.: +852 27 666 537; fax: +852 27 731 1432. 0166-3615/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.compind.2005.04.003

properties is coarse to model the goodness in fabric hand. It is only an objective metric which does not encounter the different psychological effects from individuals. Individuals prefer to use their sensory feelings instead of the mechanical properties to evaluate the fabric performance, because it is more convenient and direct. In this study, we have developed a fuzzy fabric advisory system which could provide the most appropriate fabric satisfying individual desires for fabric hand. This paper is organized as follows. Section 2 reviews the literature regarding the fabric hand descriptors and the framework of the standard fuzzy system on the fabric selection. Section 3 outlines the research methodology involving the identification of the sensory knowledge on the fabric hand descriptors under the selected woven fabrics and the design of the proposed fuzzy fabric advisory system with fine-tuning algorithm to refine the system performance. Section 4 presents the experimental results. Finally, the last section concludes the findings and describes the limitations of the current study as well as providing some suggestions for future research in this area. 2. Literature review In this section, we review the subjective fabric hand descriptors and previous works using artificial intelligent

Applied Soft Computing 7 (2007) 1177–1187 www.elsevier.com/locate/asoc

A neural clustering and classification system for sales forecasting of new apparel items Se´bastien Thomassey *, Michel Happiette GEMTEX-ENSAIT, 9 rue de l’ermitage, 59100 Roubaix, France Available online 28 February 2006

Abstract The Textile-Apparel-Distribution network actors require a very accurate production and sourcing management to minimize their costs and satisfy their customers. For a such strategy, distributors rely on sales forecasting system to respond to the versatile textile market. However, the specific constraints of the textile sales (numerous and new items, short lifetime) complicate the forecasting procedure and distributors prefer to use intuitive estimation methods of the sales rather than the existing forecasting models. We propose a decision aid system, based on neural networks, which automatically performs item sales forecasting. Performances of our model are evaluated using real data from an important French textile distributor. # 2006 Elsevier B.V. All rights reserved. Keywords: Sales forecasting; Clustering; Classification; Neural networks

1. Introduction These last decades, methods based on Supply Chain Management tools (Manufacturing Requirement Planning, Distribution Requirement Planning, Enterprise Resource Planning), have enabled an improvement of the sourcing, production and distribution of the textile items. However, due to the competitive environment, the globalization, the irreducible manufacturing lead times and the uncertainty of the customer’s demand, the sales forecast is a fundamental success factor of the supply chain optimization of apparel companies [47]. The forecasting system must deal with the constraints of the textile market:

Large number of items (about 15,000 per year). Items with short lifetimes (6–12 weeks). Substitution of most of the items for each collection (95%). Long lead time of textile items requires considerations of producing and planning of sourcing at a mid-term horizon (the forecasting horizon is one season or 1 year). Influence of many explanatory variables. These factors can be: weather data, holiday, marketing action, promotions, fashion, economic environment. * Corresponding author. Tel.: +33 3 20 25 64 64; fax: +33 3 20 27 25 97. E-mail address: sebastien.thomassey@ensait.fr (S. Thomassey). 1568-4946/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.asoc.2006.01.005

Several forecasting models, such as regression models [51,24,7], exponential smoothing and Box & Jenkins models [52,21,58], neural networks [74,77,50] or fuzzy systems [15,46,11], have been developed and provide satisfactory results in different domains [36]. However, their performances strongly depend of the field of application, the forecasting goal, the user experience, and the forecast horizon [4,13] and these methods are not easily usable in the specific textile environment. Preceding works are enabled us to carry out a global forecasting system for textile sales forecasting. This system, based on soft computing techniques, is composed of several models which automatically performs mid- [64,65] and shortterms sales forecasting [66]. However, due to the substitution of most items for each collection, the aggregation of sales by items families or by clustering procedures to obtain complete historical data of several years is required. This paper deals with the mid-term sales forecasting for items for which we have no historical sales data. Thus, we propose to improve the system by combining clustering and classification tools. The uncertainty and the complex relationship between the sales and the descriptive criteria of items lead us to rely on neural techniques. Section 2 describes the proposed forecasting system. Section 3 reports and analyzes the empirical results obtained with real data supplied by an important French ready-to-wear distributor.

Available online at www.sciencedirect.com

Expert Systems with Applications Expert Systems with Applications 36 (2009) 2037–2047 www.elsevier.com/locate/eswa

A hybrid model using genetic algorithm and neural network for classifying garment defects C.W.M. Yuen a, W.K. Wong a,*, S.Q. Qian a, L.K. Chan a, E.H.K. Fung b b

a Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Kowloon, Hong Kong Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong

Abstract The inspection of semi-finished and finished garments is very important for quality control in the clothing industry. Unfortunately, garment inspection still relies on manual operation while studies on garment automatic inspection are limited. In this paper, a novel hybrid model through integration of genetic algorithm (GA) and neural network is proposed to classify the type of garment defects. To process the garment sample images, a morphological filter, a method based on GA to find out an optimal structuring element, was presented. A segmented window technique is developed to segment images into several classes using monochrome single-loop ribwork of knitted garment. Four characteristic variables were collected and input into a back-propagation (BP) neural network to classify the sample images. According to the experimental results, the proposed method achieves very high accuracy rate of recognition and thus provides decision support in defect classification. Ó 2008 Elsevier Ltd. All rights reserved. Keywords: Image segmentation; Morphological filter; Genetic algorithms; Neural network; Garment inspection

1. Introduction Although clothing manufacturers have devoted a great deal of effort and investment to implement systematic training programs for sewing operatives before they are assigned to work on the production floor, the sizing, stitching and workmanship problems can still be found during the on-line and final inspections. Quality inspection of garments is an important aspect of clothing manufacturing. However, defect detection is usually done by human inspectors, and results are greatly influenced by their mental and physical conditions. Therefore, automatic inspection systems (AIS) are becoming fundamental to advanced manufacturing. There have been a lot of studies of fabric inspection techniques to detect defects since the last two decades. Shimizu, Ishikawa, and Kayama (1990) used an expert system to recognize fabric defects. A Gaussian Markov random *

Corresponding author. Tel.: +852 2766 6471; fax: +852 2773 1432. E-mail address: tcwongca@inet.polyu.edu.hk (W.K. Wong).

ﬁeld was also used to inspect fabric defects (Cohen, Fan, & Attai, 1991). Jasper and Potlapalli (1995) reported that the wavelet transform gave better results than the Sobel edge operating and the fast Fourier transform in terms of fabric defect detection. Chen, Liang, Yau, Sun, and Wang (1998) used a BP neural network with power spectra to classify textiles. Shiau, Tsai, and Lin (2000) classiﬁed web defects with a BP neural network by color image processing. A method of laser-based morphological image processing was studied to detect fabric defects (Goswami & Datta, 2000). A survey of several techniques available for the inspection of textured surfaces could also be found (Kumar, 2001). Now there are three AIS of fabric, namely Barco Vision’s Cyclops, Elbit Vision Systems’s I-Tex and Zellweger Uster’s Fabriscan, which are available on the market. Most of the past research was about fabric inspection or general web material inspection, and there were few on garment inspection. The development of automatic garment inspection to replace manual inspection in the clothing

Expert Systems with Applications 36 (2009) 11875–11887

Contents lists available at ScienceDirect

Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa

A Hybrid Fuzzy Knowledge-Based Expert System and Genetic Algorithm for efﬁcient selection and assignment of Material Handling Equipment S. Hamid L. Mirhosseyni *, Phil Webb School of Mechanical, Materials and Manufacturing Engineering, The University of Nottingham, University Park, Nottingham NG7 2RD, UK

a r t i c l e

i n f o

Keywords: Material Handling Equipment Fuzzy Knowledge-Based Expert System Genetic Algorithm Artiﬁcial intelligence

a b s t r a c t Material Handling (MH) is one of the key issues for every production site and has a great impact on manufacturing costs. The core concern in the design of a MH system is selecting the most suitable equipment for every MH operation and optimising them totally in order to attain an optimum solution. This paper presents a hybrid method for the selection and assignment of the most appropriate Material Handling Equipment (MHE). In the ﬁrst phase, the system selects the most appropriate MHE types for every MH operation in a given application using a Fuzzy Knowledge-Based Expert System consisting of two sets of rules: Crisp Rules and Fuzzy Rules. In the second phase, a Genetic Algorithm (GA) searches throughout the feasible solution space, constituting of all possible combinations of the feasible equipment speciﬁed in the previous phase, in order to discover optimum solutions. The validity of the methodology developed in this paper is proved through the use of a real problem. Finally a comparison of the method with the other available publicised methods reveals the effectiveness of this hybrid approach. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction A Material Handling (MH) system is responsible for transporting materials between workstations with minimum obstruction and joins all workstations and workshops in manufacturing systems by acting as a basic integrator (Sujono & Lashkari, 2007). According to our definition, ‘‘MH is the art of implementing movement economically and safely” (Apple, 1972). The key role of a MH system in industry is apparent simply because without it the movement of materials between processes is impossible and production therefore could not be accomplished. Additionally, the MH cost is a substantial component of the total costs in manufacturing. Tompkins et al. (1996) estimated that in a typical manufacturing operation, 25% of the number of employees, 55% of all plant area, and 87% of production time are assigned to MH and it accounts for between 15% and 70% of the total cost of manufacturing a product. In summary, an efficient MH system greatly improves the competitiveness of a product through the reduction of handling cost, enhances the production process, increases production and system flexibility, provides effective utilisation of manpower and decreases lead time (Chan, 2002; Chu, Egbelu, & Wu, 1995). Having an efficient and cost-effective MH system necessitates designing

* Corresponding author. Tel.: +44 1159786157. E-mail addresses: h_mirhosseyni@hotmail.com, (S.H.L. Mirhosseyni).

epxshl@nottingham.ac.uk

the entire MH system at once even though it may comprise several subsystems. The selection and configuration of Material Handling Equipment (MHE) types are the key subsystems in the design of a MH system (Chan, 2002; Park, 1996). Since the 1970s research concentrating on the selection and assignment of MHE has been carried out and significant achievements have been attained. The vast majority of the research has addressed only the selection problem whilst there only a small amount aimed at developing methods for the resolution of both the selection and assignment problem and to reach a comprehensive solution for the whole MH problem. This paper presents a novel two-phase method employing a Hybrid Fuzzy Knowledge-Based Expert System and Genetic Algorithm to practically solve both the selection and assignment of MHE problem. In the first phase a Fuzzy Expert System is used to identify the best MHE types for every handling operation with their appropriateness factors whilst in the second phase a GA investigates throughout the feasible solution space to select a number of optimal solutions. This paper is organised as follows. Section 2 presents an overview of some of the related literature. In Section 3 the MHE selection and assignment problem is reviewed in a total view. While the methodology framework is presented in Sections 4–6, respectively, discuss the first and the second phase of the method in detail. The software developed is outlined in Section 7, and the capacity analysis together with the comparison of the method with other techniques is discussed in Section 8. Finally the last section concludes the discussion of this paper.

ARTICLE IN PRESS

Int. J. Production Economics 96 (2005) 81–95

A global forecasting support system adapted to textile distribution Se! bastien Thomassey*, Michel Happiette, Jean-Marie Castelain GEMTEX-ENSAIT, 9 rue de l’Ermitage, BP 30329, Roubaix cedex 1 F-59056, France Received 20 February 2003; accepted 8 March 2004

Abstract Competition and globalization imply a very accurate production and sourcing management of the Textile–Apparel– Distribution network actors. A sales forecasting system is required to respond to the versatile textile market and the needs of the distributor. Nowadays, the existing forecasting models are generally unsuitable to the textile industry. We propose a forecasting system, which is composed of several models and performs forecasts for various horizons and at different sales aggregation levels. This system is based on soft computing techniques such as fuzzy logic, neural networks and evolutionary procedures, permitting the processing of uncertain data. Performances of our models are then evaluated using the real data from an important French textile distributor. r 2004 Elsevier B.V. All rights reserved. Keywords: Sales forecasting; Textile distribution; Soft computing

1. Introduction As in other competitive industries, companies in the Textile–Apparel–Distribution network require the rigorous management of sourcing, production and distribution. Supply chain management (SCM) (Lee and Sasser, 1995), includes all these processes from the expression of the demand until delivery of the ﬁnished products. This SCM concept uses tools which intervene at various steps and places in the logistic chain (GPA, MRP, DRP, ERP, etc.) and include different functions, such as purchasing, sourcing, production planning, inventory control and exchanges of information. How*Corresponding author. E-mail address: sebastien.thomassey@ensait.fr (S. Thomassey).

ever, even if existing methods improve the reactivity of Textile–Apparel–Distribution network, many transformations, which are required to produce textile item, always impose signiﬁcant and not easily reducible manufacturing lead times. Globalization, which causes dispersion of network actors, also increases these lead times. Thus, in order to deal with the customer’s requests, companies often need to anticipate production and to produce items for stock. The main goal of the distributors is to offer the right product, at the right place and the right price, while maintaining the right stock. These constraints require an appropriate sales forecasting system. For the distributor, the correct anticipation of requests from the consumer allows the upstream companies to provision and adjust their production. Thus, the effectiveness of the supply

Computers & Industrial Engineering 50 (2006) 175â&#x20AC;&#x201C;184 www.elsevier.com/locate/dsw

A genetic algorithm for solving the two-dimensional assortment problem Chang-Chun Lin * Department of Information Management, Kun-Shan University, No. 949, Da-Wan Road, Yung-Kang City, Tainan 710, Taiwan, ROC Received 11 October 2003; received in revised form 8 March 2006; accepted 8 March 2006 Available online 2 May 2006

Abstract Assortment problems arise in various industries such as the steel, paper, textiles and transportation industries. Twodimensional assortment problems involve finding the best way of placing a set of rectangles within another rectangle whose area is minimized. Such problems are nonlinear and combinatorial. Current mixed integer programming models give optimal solutions, but the computation times are unacceptable. This study proposes a genetic algorithm that incorporates a novel random packing process and an encoding scheme for solving the assortment problem. Numerical examples indicate that the proposed genetic algorithm is considerably more efficient and effective than a fast integer programming model. Errors with respect to the optimal solutions are low such that numerous practical industrial cutting problems can be solved efficiently using the proposed method. q 2006 Elsevier Ltd. All rights reserved. Keywords: Assortment problem; Genetic algorithm; Random bottom-left procedure

1. Introduction Two-dimensional assortment problems arise when a given set of rectangles are to be cut from a larger rectangle whose area is to be minimized, or when a set of rectangles are to be packed within a enveloping rectangle of minimal area. A problem that is similar to the assortment problem is the trim-loss problem (Hinxman, 1980). Assortment and trim-loss problems arise in various industries such as the steel, paper, textiles and transportation industries. One application of the assortment problem concerns the layout design of a set of departments (Buffa, Armour, & Vollman, 1964; Seehof & Evans, 1967). In a trim-loss problem, the sizes of the material stocks from which smaller pieces are cut off are predetermined, unlike the size of the enveloping rectangle in an assortment problem. All such problems can be classified as one-dimensional, 11â &#x201E;2 -dimensional and two-dimensional (Hinxman, 1980). However, 11â &#x201E;2 -dimensional problems can be viewed as special cases of two-dimensional problems. Thus, this study focuses mainly on twodimensional assortment problems. The assortment problem has been studied less than the trim-loss problem. Page (1975) first recognized the assortment problem in relation to the cutting of steel bars and developed a dynamic programming formulation. Meanwhile, Chambers and Dyson (1976) considered a version of the two-dimensional problem in which possible stock size widths and lengths are integers in given ranges. Beasley (1985) developed an integer model and a heuristic * Fax: C886 6 273 2726. E-mail address: chanclin@ms29.hinet.net.

oornpu~ induata-lal

engirmm, lng PERGAMON

Computers & Industrial Engineering 37 (1999) 375-378

A Genetic Algorithm for a 2D Industrial Packing Problem E. Hopper, B. Turton

University of Wales, Cardiff, School of Engineering, Electronic Division, Newport Road, Cardiff, CF2 3TD, UK, tel. +44-1222-874425, fax: +44-1222-874716, HopperE @cf.ac.uk, Turton@cf.ac.uk Cutting and packing problems are encountered in many industries, with different industries incorporating different constraints and objectives. The wood-, glass- and paper industry are mainly concerned with the cutting of regular figures, whereas in the ship building, textile and leather industry irregular, arbitrary shaped items are to be packed. In this paper two genetic algorithms are described for a rectangular packing problem. Both GAs are hybridised with a heuristic placement algorithm, one of which is the well-known Bottom-Left routine. A second placement method has been developed which overcomes some of the disadvantages of the Bottom-Left rule. The two hybrid genetic algorithms are compared with heuristic placement algorithms. In order to show the effectiveness of the design of the two genetic algorithms, their performance is compared to random search. ÂŠ 1999 Elsevier Science Ltd. All rights reserved.

Keywords: two-dimensional orthogonal packing problem, nesting, combinatorial optimisation, genetic algorithms, random search, heuristics, simulation INTRODUCTION Packing problems are optimisation problems that are concerned with finding a good arrangement of multiple items in larger containing regions (objects). The usual objective of the allocation process is to maximise the material utilisation and hence to minimise the "wasted" area. This is of particular interest to industries involved with mass-production as small improvements in the layout can result in savings of material and a considerable reduction in production costs. The development of an algorithm to solve an industrial packing problem clearly must consider the complexity of the problem, determined by the geometry of the objects and the constraints imposed. In addition the algorithm must be easy to adapt to the present competitive market with frequent product introductions, changing product designs and "shorter time to market" strategy. The flexibility achieved by manual packing is no longer a competitive solution due to high labour and liability costs. Conventional automated packing methods do not offer this flexibility, since they are mostly tailored to a particular packing task (Hinxman, 1980; Satin, 1983; l-I~sler and Sweeney, 1991). This calls for a new approach to packing problems, which implements automation, but also maintains the flexibility which is offered by manual composition of packing layouts. One of the main aspects in the development of flexible packing systems is the integration of intelligent search processes in order to find good packing patterns. Intelligent search processes such as genetic algorithms are highly flexible since they describe the packing problem in the form of general search principles rather than a set of special placement rules. Our work is concerned with a two-dimensional packing problem frequently encountered in the wood-, glass- and paper industry. The problem consists of packing rectangular items onto a rectangular object while minimising the used object space. The packing process has to ensure that there is no overlap between the items, which are allowed to rotate by 90 Â° . So far only a few researchers have applied genetic algorithms to this problem type. Genetic algorithms for packing problems mainly concentrate on guillotineable packing problems (KrOger, 1995; Andr~ls, 1996) and bin-packing (Hwang, 1992; Falkenauer, 1994). Smith (1985) developed an order-based genetic for a rectangle packing problem, where the orientation of the items is fixed. The genetic algorithm by Kr6ger et al. (1991) includes rotation and is based on a tree structure to encode the problem. Since its performance is compared to well-known packing heuristics, a relative comparison with our technique is possible. 0360-8352/99 - see front matter ÂŠ 1999 Elsevier Science Ltd. All rights reserved, PII: S0360-8352(99)00097-2

ARTICLE IN PRESS Expert Systems with Applications xxx (2009) xxx–xxx

Contents lists available at ScienceDirect

Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa

A GA methodology for the scheduling of yarn-dyed textile production Hsi-Mei Hsu a,*, Yai Hsiung b,1, Ying-Zhi Chen a,2, Muh-Cherng Wu a,3 a b

Department of Industrial Engineering and Management, National Chiao Tung University, Hsin-Chu, Taiwan, ROC Department of Information Management, Ta Hwa Institute of Technology, Hsin-Chu, Taiwan, ROC

a r t i c l e

i n f o

Keywords: Scheduling Sequence-dependent setup Multi-stage Textile Genetic algorithm Group-delivery

a b s t r a c t This paper presents a scheduling approach for yarn-dyed textile manufacturing. The scheduling problem is distinct in having four characteristics: multi-stage production, sequence-dependent setup times, hierarchical product structure, and group-delivery (a group of jobs pertaining to a particular customer order must be delivered together), which are seldom addressed as a whole in literature. The scheduling objective is to minimize the total tardiness of customer orders. The problem is formulated as a mixed integer programming (MIP) model, which is computationally extensive. To reduce the problem complexity, we decomposed the scheduling problem into a sequence of sub-problems. Each sub-problem is solved by a genetic algorithm (GA), and an iteration of solving the whole sequence of sub-problems is repeated until a satisfactory solution has been obtained. Numerical experiment results indicated that the proposed approach signiﬁcantly outperforms the EDD (earliest due date) scheduling method—currently used in the yarn-dyed textile industry. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction Yarn-dyed textiles are distinct in their manufacturing processes in which yarn must be dyed before weaving, while most other textiles are ﬁrst woven and then dyed. A yarn-dyed textile product, for example a shirt, contains several patterns cloths. A pattern cloth manifests itself by a particular pattern of colors. In a colorful shirt, its sleeve may be a single-color pattern while its pocket may be a three-color pattern. A three-color pattern is composed of three different color yarns, with each color yarn being individually dyed. Only when the three different color yarns have been dyed, they could be weaved into the three-color pattern cloth. Group-delivery is an essential characteristic in the dyeing process. Referring to the shirt shown in Fig. 1, we have ﬁve different color yarns to be dyed in the dyeing stage. To weave each pattern cloth, all its composing yarns have to be delivered to the weaving machine in a group manner. That is, only when all the composing yarns of a particular pattern cloth arrive at the weaving machine, can the weaving of the pattern cloth be carried out. Likewise, group-delivery is also an essential characteristic in the weaving process. See the shirt shown in Fig. 1, we have three pattern-cloths to be woven. For effectively making the shirt, the three * Corresponding author. Tel.: +886 3 5731761. E-mail addresses: hsimei@cc.nctu.edu.tw (H.-M. Hsu), yai@thit.edu.tw (Y. Hsiung), yzchen@mail.nctu.edu.tw (Y.-Z. Chen), mcwu@mail.nctu.edu.tw (M.-C. Wu). 1 Tel.: +886 3 5927700x2751; fax: +886 3 5923957. 2 Tel.: +886 3 5731761. 3 Tel.: +886 3 5731913.

pattern-cloths also have to be delivered in a group manner. That is, only when all the three pattern-cloths have shipped to the downstream shirt-maker, can the shirt-maker starts to manufacture the shirt. In addition, the dyeing process is distinct in having a setup dependency characteristic. Before dyeing a yarn, we need to clean the dyeing tank—the machine that processes the yarn to be dyed. The clean time (setup time) required to prepare for dyeing a coming job can be different, dependent upon the colors of the coming yarn and the one just ﬁnishing dyeing. Consider two consecutive dyeing jobs. If the preceding job is dark-color (e.g. black) and the following one is light-color (e.g. yellow), then we need a thorough cleaning for the dyeing tank. That is, before dyeing the light-color job, the dark-coloring agent in the tank should be completely removed. In contrast, if the preceding job is light-color and the following one is dark-color, then we need only a rough cleaning for the dyeing tank. The time required for a thorough cleaning is much longer than that for a rough cleaning. This feature indicates that the dyeing process is sequence-dependent in setup time. In summary, the manufacturing of the yarn-dyed textiles essentially involves two consecutive production processes—dyeing and weaving. These two processes are distinct in three points: (1) group-delivery in the dyeing process, (2) group-delivery in the weaving process, and (3) sequence-dependent in the dyeing process. To our knowledge, scheduling problems concerning these three features as a whole have not been examined in literature. This paper formulated the scheduling problem for the yarndyed textile manufacturing process as a mixed integer program, and developed a genetic algorithm based approach to solve the

Please cite this article in press as: Hsu, H.-M., et al. A GA methodology for the scheduling of yarn-dyed textile production. Expert Systems with Applications (2009), doi:10.1016/j.eswa.2009.04.075

Computers and Mathematics with Applications 53 (2007) 1840–1846 www.elsevier.com/locate/camwa

A fuzzy neural network model for predicting clothing thermal comfort Xiaonan Luo a , Wenbang Hou a,∗ , Yi Li b , Zhong Wang b a Computer Application Institute, Sun Yat-sen University, Guang Zhou 510275, China b Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

Received 3 November 2005; received in revised form 29 June 2006; accepted 11 October 2006

Abstract This paper presents a Fuzzy Neural Network (FNN) based local to overall thermal sensation model for prediction of clothing thermal function in functional textile design system. Unlike previous experimental and regression analysis approaches, this model depends on direct factors of human thermal response — body core and skin temperatures. First the local sensation is predicted by a FNN network using local body part skin temperatures, their change rates, and core temperature as inputs; then the overall sensation is predicted. This is also performed by a FNN network. The FNN networks are developed on the basis of the Feed-Forward BackPropagation (FFBP) network; the advantage of using fuzzy logic here is to reduce the requirement of training data. The simulation result shows a good correlation between predicted and the traditional experimental data. c 2007 Elsevier Ltd. All rights reserved. Keywords: Thermal sensation; Functional textile design; Fuzzy neural network; Feed-forward back-propagation; Clothing thermal comfort

1. Introduction Today numerous consumers consider thermal comfort to be one of the most significant attributes when purchasing textile and apparel products, so there is a need to develop a functional garment CAD system. In recent years, many textile thermal function models and simulation systems have been developed [1–3]. They can simulate human thermal physiological status and clothing heat and moisture transfer processing for designated arbitrary garment constructions and thermal environments. Because the human–clothing environment is a transient and non-uniform thermal environment, up to now there has been no appropriate thermal comfort model to evaluate clothing thermal comfort. The existing literatures on human thermal sensation and comfort are generally focused on steady-state and uniform conditions. Representatives are Fanger’s PMV (Predicted Mean Vote) model [4] and Gagge’s two-node model with its indices of TSENS (Thermal Sensation) and DISC (Thermal Discomfort) [5]. They are the basis of ASHRAE Standard 55-1992 and ISO EN 7730 Standard. There are also works addressing transient and non-uniform conditions separately [6,7]. The above models are usually aimed at representing relationships between environment conditions and human thermal responses. In ∗ Corresponding author. Tel.: +86 20 3402 2313.

E-mail addresses: lnslxn@mail.sysu.edu.cn (X. Luo), houwenbang@yahoo.com.cn (W. Hou), tcliyi@inet.polyu.edu.hk (Y. Li). c 2007 Elsevier Ltd. All rights reserved. 0898-1221/$ - see front matter doi:10.1016/j.camwa.2006.10.035

ARTICLE IN PRESS

Int. J. Production Economics 114 (2008) 594–614 www.elsevier.com/locate/ijpe

A framework of E-SCM multi-agent systems in the fashion industry Wei-Shuo Loa, , Tzung-Pei Hongb, Rong Jengc a

Department of Public Finance, Meiho Institute of Technology, Ping-Tung, Taiwan Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan c Department of Information Management, I-Shou University, Kaohsiung, Taiwan

Received 7 September 2006; accepted 1 September 2007 Available online 10 March 2008

Abstract The fashion industry’s supply chain is full of uncertainty and unpredictability. Thus, building an intelligent system to effectively capture the requirements of customers and help manage the supply chain is very important. Typical quick response (QR) systems have been broadly used in the fashion industry to serve as a way of maintaining an efﬁcient supply chain management (SCM). The original functions of a QR system cannot, however, completely overcome the challenge of quickly satisfying the requirements of customers with effective customer relationship and quality of service. In this paper, we have integrated the typical management information system (MIS) development procedure with that of an e-fashion SCM multi-agent system. Some related research and reports from different countries have been thoroughly surveyed in order to ﬁnd possible IT and non-IT methods for use in the SCM of fashion retailers. This paper thus provides an electronic fashion SCM system by adopting the techniques of the Semantic Web and multiple agents. The proposed system can integrate different information technologies to make its behavior more intelligent and to catch more useful information from customers. Its implementation also considers some practical issues in the fashion retailing SCM. r 2008 Elsevier B.V. All rights reserved. Keywords: Fashion industry; E-SCM system; Semantic web; Multiple agents

1. Introduction The fashion industry has faced more and faster changes in recent years due to the different requirements of customers and the variations of global economic environments. This industry usually needs to produce or provide various, complex, Corresponding author. Tel.: +886 8 7799821x8500;

fax: +886 8 7788118. E-mail addresses: x2134@meiho.edu.tw (W.-S. Lo), tphong@nuk.edu.tw (T.-P. Hong), rjeng@isu.edu.tw (R. Jeng).

and fashionable textile products, such as fashion outerwear, fashion wear, indoor and outdoor sportswear, fashion textiles, interior textiles, textile design, working wear, and so on. The different requirements of textile products may arbitrarily appear at any fashion market. These requirements and the order information are then delivered from each sale company of textile products to its upstream sale company or manufacturers. The upstream sale companies or manufacturers then refer to the speciﬁcations, prices, colors, and time the orders were received to ﬁnish

Available online at www.sciencedirect.com

Expert Systems with Applications Expert Systems with Applications 36 (2009) 1750–1764 www.elsevier.com/locate/eswa

A fashion mix-and-match expert system for fashion retailers using fuzzy screening approach W.K. Wong a,*, X.H. Zeng b, W.M.R. Au a, P.Y. Mok a, S.Y.S. Leung a a

Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong b College of Information Engineering, Dong Hua University, Shanghai 200051, China

Abstract In today’s fashion retailing business, providing ‘‘fashion mix-and-match” or ‘‘fashion coordination” recommendations is a ‘must’ strategy to enhance customer service and improve sales. In this study, a fashion mix-and-match expert system is developed to provide customers with professional and systematic mix-and-match recommendations automatically. The system can capture the knowledge and emulate the decisions of fashion designers on apparel coordination and its knowledge base can store the literal form of information. A set of attributes of the apparel for coordination are identified and formulated; their corresponding importance is also defined with designers’ opinions using ordered weighted averaging operators. The Fashion Coordination Satisfaction Index is devised and computed using the fuzzy screening approach to represent the satisfaction degree of the coordinating pairs of apparel product items. The experimental results demonstrate that the proposed system can generate effective mix-and-match recommendations and is now integrated with a smart dressing system used effectively in a fashion chain store company in Hong Kong. Ó 2008 Elsevier Ltd. All rights reserved. Keywords: Expert systems; Fuzzy screening; Multi-criteria decision-making

1. Introduction Customer service plays a vitally important role in today’s fashion retailing business. Strategies like private sales and VIP memberships are used by nearly every fashion retailer to enhance customers’ brand loyalty. Providing mix-andmatch recommendations is therefore a ‘must’ strategy for retailers to enhance customer service and improve sales. Mix-and-match recommendations are traditionally given by individual sales personnel based on their experience and/or designers’ suggestions, by either showing customers photos in the product catalogues or locating the matching products on racks. With the recent invention of a smart dressing system by the authors, fashion items carried by customers can automatically be detected with mix-and-match recommendations shown in real-time (Hkpolyu, 2006; Itc, 2007; Wong, Leung, & Mok, 2006a, Wong, Leung, & *

Corresponding author. Tel.: +852 2766 6471; fax: +852 2773 1432. E-mail address: tcwongca@inet.polyu.edu.hk (W.K. Wong).

Mok, 2006b). Such an invention revolutionizes fashion retail operations and enhances customer service. The smart dressing system makes use of the technology of radio frequency identification (RFID) to detect items brought into a fitting room or placed in front of a dressing mirror. Each product item bears a RFID tag. When an item is picked into the fitting room or placed in front of a dressing mirror, the product will be immediately detected and transmitted to the system through the antennae and reader. The mix-andmatch database of the invention will then deliver recommendations to the customer through a touch-screen LCD monitor or projected screen (For details, please see references Hkpolyu, 2006; Itc, 2007; Wong et al., 2006a, 2006b.). The recommendations stored in this mix-andmatch database are provided by the proposed fashion mix-and-match expert system in this paper for emulating fashion designers to generate and export the mix-and-match recommendations to the database. In the past, fashion designers create a collection of fashion products and also determined how to coordinate and

Available online at www.sciencedirect.com

Expert Systems with Applications Expert Systems with Applications 36 (2009) 2377–2390 www.elsevier.com/locate/eswa

A decision support tool for apparel coordination through integrating the knowledge-based attribute evaluation expert system and the T–S fuzzy neural network W.K. Wong a,*, X.H. Zeng b, W.M.R. Au a a

Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong b College of Information Engineering, Dong Hua University, Shanghai 200051, China

Abstract In today’s competitive fashion retailing business, providing ‘‘mix-and-match” or ‘‘fashion coordination” recommendations can enhance customer service, brand loyalty and improve sales. In this study, we propose a decision support tool for fashion coordination through the integration of the knowledge-based attribute evaluation expert system and the Takagi–Sugeno fuzzy neural network (TSFNN). A set of attributes of the apparel items for coordination are identified and formulated. The evaluation of these attributes can be accomplished by a knowledge-based expert system which can handle the difficulty of processing linguistic and categorical information effectively. A fuzzy clustering technique and a new hybrid learning algorithm combining the PSO and GA techniques are proposed to reduce the coordination rules and the training time for the TSFNN. The experimental results show that rules reduction can shorten the TSFNN training time while keeping a very satisfactory and low MSE value. The proposed hybrid algorithm outperforms the Back Propagation, the Genetic Algorithm, and the Particle Swarm Optimization. The apparel pairs recommended by the decision support tool are now integrated with a smart dressing system of a fashion retailing company in Hong Kong and practically used. Ó 2008 Elsevier Ltd. All rights reserved. Keywords: Fuzzy neural networks; Particle swarm optimization; Genetic Algorithm; Decision support; Back Propagation

1. Introduction Customer service plays a vitally important role in today’s fashion retailing business. Strategies like private sales and VIP membership are used by nearly every retailer to enhance customers’ brand loyalty. Providing apparel coordination, also known as ‘‘mix-and-match” recommendations, is therefore a ‘must’ strategy for retailers to enhance customer service and improve sales. The word ‘coordination’ means ‘‘harmonious combination or interaction, as of functions or parts” (Dictionary.com, 2006). We can further say that apparel coordination means to combine in a harmonious or interesting way, as articles

Corresponding author. E-mail address: tcwongca@inet.polyu.edu.hk (W.K. Wong).

of clothing in an ensemble. Mix-and-match recommendations are traditionally given by individual sales personnel based on their experience and/or designers’ suggestions, by either showing customers photos in the product catalogue or locating the coordination products on racks. With the recent invention of a smart dressing system by the authors (Wong, Leung, & Mok, 2006a,b) fashion items carried by customers can automatically be detected with mixand-match recommendations shown in real time. Such an invention revolutionizes fashion retail operation and enhances customer service (Hkpoly, 2006; Itc, 2007). The smart dressing system makes use of the technology of Radio Frequency Identification (RFID) to detect items brought into a fitting room or placed in front of a dressing mirror. Each product item bears an RFID tag. When an item is brought into a fitting room or placed in front of a dressing mirror, the product will be immediately detected

The current issue and full text archive of this journal is available at http://www.emerald-library.com/ft

IJCST 13,2

106 Received September 1999 Revised January 2001 Accepted January 2001

Total handle evaluation from selected mechanical properties of knitted fabrics using neural network Shin-Woong Park, Young-Gu Hwang and Bok-Choon Kang Department of Textile Engineering, Inha University, South Korea, and

Seong-Won Yeo

Department of Electrical Engineering, Inha University, South Korea Keywords Mechanical properties, Fuzzy logic, Neural networks, Knitwear, Simulation Abstract This paper concentrated on the objective evaluation of total hand value in knitted fabrics using the theory of neural networks and the comparison of two methods. For the objective evaluation of overall hand feeling in knitted fabric, 47 kinds of weft-knitted and warp-knitted fabrics were manufactured. The optimum construction of neural networks was investigated through the change of layer and neuron number. For the comparison of the two methods, a subjective test was carried out. Two techniques, KES-FB system and neural network applied simulator, were compared using nine randomly selected knitted fabrics. These fabrics were used to show that the neural network adapted simulation method was in good agreement with subjective test results.

International Journal of Clothing Science and Technology, Vol. 13 No. 2, 2001, pp. 106-114. # MCB University Press, 0955-6222

1. Introduction Fabric hand has been considered as one of the most important performance attributes of textiles intended for use in garments. Methods for predicting knitted fabrics in apparel manufacture from its physical, mechanical and dimensional properties have been investigated (Kawabata, 1980; Gong, 1995; Park and Hwang, 1999). In previous papers (Park and Hwang, 1999; Park et al., 1996; 1997; 1998), we have published data regarding fuzzy predicting model of woven, warp-knitted and double weft-knitted fabrics and have studied a fuzzy applied method and a neural network applied simulation. A subjective test gave a good agreement with results of the fuzzy model and the neural network prediction simulator rather than that of KES-FB system. But fuzzy method is not a simulator, but a mathematical modeling equation. We have investigated the neural network applied total handle evaluator, which is a method setting fuzzy mathematical results as a target output. Therefore, it was recognized that there is a need to establish an exact automatically hand evaluation system, being based on subjective test results. In this paper, we extend our investigations to the objective hand evaluation of knitted fabrics used for fall and winter, which included various type of fibers and their constructions of single, double and warp-knitted fabrics. The authors wish to thank the Inha University and Industrial Technology Research Institute Foundation for the financial support provided for this study.

477

in Garment Artificial Neural Networks

Predicting the Performance of Fabrics

Manufacturing

with

R. H. GONG

Department of Textiles, University of Manchester Institute of Science and Technology, United Kingdom

Manchester M60

1QD,

Y. CHEN

Department of Silk

Textile

Engineering, Suzhou Institute of Silk Peopleâ&#x20AC;&#x2122;s Republic of China

Textile

Technology, Suzhou,

ABSTRACT

Neural networks are used to predict the performance of fabrics in clothing manufacturing. The predictions are based on fabric mechanical properties measured on the KES-FB system. The influence of the number of input and hidden nodes on the convergence speed and the prediction accuracy are investigated. Tests indicate that these artificial neural networks are effective for predicting potential problems in clothing manufacturing.

In recent years, garment manufacturing processes have become more and more automated, the consumer market is increasingly sophisticated, demanding more choices, and there is an expanding variety of fabrics that manufacturers have to process into different styles of garments. Quality control in garment manufacturing is therefore becoming more difficult. In the last decade, greater attention has been paid to the influence of fabric properties in garment processing. In order to obtain good quality products with high efficiency production lines, clothing companies have established advanced laboratories to measure fabric properties for controlling fabric quality, production processes, and garment quality [2, 6]. Many researchers have also been working on the relationships between fabric properties and performance in clothing production in order to predict a fabricâ&#x20AC;&#x2122;s performance on the basis of its properties, especially mechanical properties under low stress as measured by the KES-FB system [3.4,5,8,9,10.11,16,17,181. Table I summarizes the fabric property parameters measured by the tcES-~ system and the areas where these properties are expected to be influential in clothing manufacturing. Several equations have also been developed for calculating tailorability, but they can only provide guidelines and cannot offer specific predictions of how a fabric might perform in garment manufacturing. The relationships between fabric performance and properties are very difficult to describe quantitatively by traditional mathematics or mechanics due to the nonlinearity of the parameters and the large number of variables involved. This, however, seems to be an ideal situation for the application of artificial neural networks (ANN), which are developed to tackle problems with large numbers of nonlinear variables.

An artificial neural network is one of the new intelligence technologies for data analysis. It imitates the behavior of biological neural networks to &dquo;learn&dquo; a subject from the data provided to it. The ANN has been successfully used in areas where a large number of factors contribute to the eventual outcome. but precise relationships between these various factors and their outcomes cannot be defined, for example, medical diagnoses and credit evaluation in banking. Attempts have recently been made to apply the ANN technique to textiles. Ramesh et al. used an ANN to predict yarn tensile properties based on yarn processing and material variables [ 14]. Pynckels et al. used an ANN to determine the spinning performance of fibers from fiber properties [ 13]. Cheng and Adams predicted yam strength according to fiber properties with ANNS [ 1 ]. Sette et al. applied this technique in the assessment of fabric set marks and carpet wear [ 15]. The applications of ANNS in these areas show great promise because an ANN can deal with the nonlinearity of problems, detect patterns and relationships in the data, and interpret information from tens or more variables. In this paper, we investigate the use of artificial neural networks to predict fabric performance in garment manufxture and the appearance of the made-up gatment. The purpose of this work is to verify the possibility of using ANN techniques in this area and the effects of different ANN architectures on their training speed and prediction accuracy. -

â&#x20AC;&#x2122;

Experimental We selected 32 fabrics with a variety of fiber compositions and fabric weaves, all made by_the industrial

Downloaded from http://trj.sagepub.com at The Hong Kong Polytechnic University on October 6, 2009

&dquo;

, ~

Computers in Industry 43 Ž2000. 1–10 www.elsevier.nlrlocatercompind

Optimization of spreading and cutting sequencing model in garment manufacturing W.K. Wong a,) , C.K. Chan a , W.H. Ip b a

Institute of Textiles and Clothing, The Hong Kong Polytechnic UniÕersity, Hunghom, Kowloon, Hongkong, People’s Republic of China Department of Manufacturing Engineering, The Hong Kong Polytechnic UniÕersity, Hunghom, Kowloon, Hongkong, People’s Republic of China Received 1 January 1999; received in revised form 1 November 1999; accepted 1 March 2000

Abstract Many researches on the machine scheduling and flowshop sequencing problem have been conducted by using genetic algorithms ŽGA.. Recently, GAs have been applied to the scheduling and line balancing problem in garment manufacturing. These applications have only been confined to the sewing operations. This paper presents a spreading and cutting sequencing ŽSCS. model using GA to solve the sequencing problem of the computerized cutting system used in the garment industry. The comparison results obtained between the actual production cycle and the proposed model using GA indicate that GA is an appropriate and effective technique to solve the problem. q 2000 Elsevier Science B.V. All rights reserved. Keywords: Optimization; Genetic algorithms; Sequencing; Computerized fabric-cutting system

1. Introduction Many studies have been made on the applications of genetic algorithms ŽGA. to the flowshop problems w2x, workshop problems w3x, line balancing w11x and travelling salesman problems w9x of different industries. Goldberg w6x also stated that GA is a robust approach, that is, a specific method better than GAs may exist for a particular instance, but on average GAs are never bad for solving a wide variety of problems. Within recent years, researches have been

)

Corresponding author. Tel.: q852-27666471; fax: q85227731432. E-mail address: tcwongca@hkpucc.polyu.edu.hk ŽW.K. Wong..

emphasized specifically on solving the scheduling and line balancing problem of sewing lines in the garment manufacturing industry. Many methods to solve these problems are exact methods, e.g. traditional linear programming, the branch and bound approach, which are time and place consuming. Approximate methods, such as heuristic methods, local improvement, etc. are also employed. Chen et al. w1x presented a heuristic solution procedure based on the simulated annealing concept to solve the daily scheduling problem in the make-to-order garment industry. Dessouky et al. w10x proposed the scheduling of multi-stage flowshops with identical jobs of a sewing line of a garment manufacturer by using branch and bound approach. Chan et al. w7x presented the line balancing problem of a sewing line by using GA. Lo w8x presented the scheduling problem of a

Int J Adv Manuf Technol (2005) 27: 152–158 DOI 10.1007/s00170-004-2161-0

ORIGINAL ARTICLE

W.K. Wong · C.K. Kwong · P.Y. Mok · W.H. Ip · C.K. Chan

Optimization of manual fabric-cutting process in apparel manufacture using genetic algorithms

Received: 28 November 2003 / Accepted: 1 March 2004 / Published online: 26 January 2005 © Springer-Verlag London Limited 2005 Abstract In apparel manufacturing, experience and subjective assessment of production planners are used quite often to plan the production schedules in their fabric-cutting departments. The quantities of cut-pieces produced by fabric-cutting departments based on these non-systematic schedules cannot fulfil the cutpiece requirements of the downstream sewing lines and minimize the makespan. This paper proposes a genetic algorithms (GAs) approach to optimize both the cut-piece requirements and the makespan of the conventional fabric-cutting departments using manual spreading and cutting methods. An optimization model for the manual fabric cutting process based on GAs was developed. Two sets of production data were collected to validate the performance of the model and the experimental results were obtained. From the results, it can be found that both the makespan and cut-piece fulfilment rates are improved in which the latter is improved significantly. Keywords Fabric-cutting · Genetic algorithms · Production scheduling

Nomenclature X N i j σi , σ j φ

Job (fabric lay) Maximum number of jobs Job setup (spreading) order and i = 1, 2, . . ., N Job processing (cutting) order and j = 1, 2, . . ., N Setup and processing sequence of jobs Production order of job X and φ = 1, 2, . . ., PO

W.K. Wong (u) · C.K. Chan Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong E-mail: tcwongca@inet.polyu.edu.hk Tel.: +852-27666471 Fax: +852-27731432 C.K. Kwong · P.Y. Mok · W.H. Ip Department of Industrial System and Engineering, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong

χ Quantity of garments of job X ϕ Length of fabric lay of job X s(X i ) Setup (spreading) time of job X i c(X j ) Processing (cutting) time of job X j m Number of spreading tables in the fabric-cutting department

1 Introduction In apparel manufacturing, fabric cutting is done before assembly. The performance of the cutting department, which is generally neglected by manufacturers, is a critical factor on the smoothness of downstream operations in sewing lines and hence the overall efficiency of the apparel manufacturing plant. Since the late 80s, some apparel manufacturers have implemented the computerized fabric-cutting systems in their apparel manufacturing process. The demands on fabric-cutting departments for greater accuracy, faster throughput, larger fabric and labour savings have driven the adoption of computerized cutting systems. However, many manufacturers still rely on the manual method for the fabric-spreading and cutting operations in their fabric-cutting department. Before daily spreading and cutting operations start, the production planners of cutting departments need to plan the production (spreading and cutting) schedule so as to minimize the idle time of operatives and fulfil the fabric cut-piece requirements from different sewing production lines. The production planning is normally based on their experience and subjective assessment which is not a systematic method and an optimal schedule cannot be obtained. As a result, idle times occur on the spreading and cutting operatives which in turn increases the overall makespan of cutting departments. The cutpiece quantities produced cannot fulfil the different requirements of each downstream sewing production line. As most of the apparel manufacturers and researchers emphasize the importance of sewing process, research has been done to improve the operation of sewing lines. However, the productivity of cutting departments, which plays a significant role

EUROPEAN JOURNAL OFOPERATIONAL RESEARCH ELSEVIER

European Journal of Operational Research 88 (1996) 165-181

Theory and Methodology

On genetic algorithms for the packing of polygons Stefan Jakobs RWTHAachen, Lehrstuhl C J~r Mathematik, Templergraben55, 17-52062Aachen, Germany ReceivedJune 1993

Abstract

A genetic algorithm for placing polygons on a rectangular board is proposed. The algorithm is improved by combination with deterministic methods. Keywords: Optimization; Genetic algorithms; Mathematical programming; Adaptive processes; Packing problems

1. Introduction and motivation

In the steel industry problems frequently occur when the need to stamp polygonal figures from a rectangular board arises. The aim is to maximize the use of the contiguous remainder of the board. Similar problems exist in the textile industry, when clothes are cut out of a rectangular piece of material. In order to solve these problems let us consider the following simpler approach. Given a finite number of rectangles ri, i = 1 , . . . , n, and a rectangular board, an orthogonal packing pattern requires by definition a disjunctive placement of the rectangles on the board in such a way that the edges of r i are parallel to the x- and y-axes, respectively. The computation of the orthogonal packing pattern with minimal height is called orthogonal packing problem (OPP). Baker, Coffman and Rivest propose an heuristic for the orthogonal packing problem; in addition they present an upper bound for the height of the packing pattern [2]. A recent survey on packing problems and their respective heuristics

is given in [16]. The extension from rectangles to polygons can be realized in several ways. The first method places the polygons directly on the board and then the algorithm optimizes locally by means of shifts and rotations [23]. A second approach places two or three polygons in a cluster. The clusters are then placed on the board [1]. In this article we use another approach, namely an evolutionary algorithm. There are three main classes in this approach, each of which is independently developed. The first class is called evolutionary programming (EP). L.J. Fogel, Owens, and Walsh were the first to develop the EP-algorithms [5]. D.B. Fogel has recently improved this approach [6]. The second class was developed by Rechenberg and Schwefel. They called their approach evolutionary strategies (ES) [17-20]. Finally, Holland developed the so called genetic algorithm (GA) [12]. The genetic algorithm has been perfected by De Jong [13] and Goldberg [9]. The paper is organized as follows. It begins by explaining the problem and its complexity. In the next section the data structure and its transformation into a packing pattern are described. Sec-

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 31, NO. 6, NOVEMBERDECEMBER 1995

1371

New Developments for Seam Quality Monitoring in Sewing Applications J. Lewis Dorrity, Member, IEEE

Abstract-The automation of sewing machinery requires that sensing be added to insure quality of the stitching. This paper describes the use of piezoelectric technology to indirectly monitor sewing thread consumption which allows an inference of seam quality. An inexpensive microprocessor system is employed to monitor the sensor. The technology developed is reliable and applicable to a wide range of sewing machinery.

I. INTRODUCTION

require even more yarn packages. The machines designed to form the stitches are ingenious and highly mechanical. Newer machines do employ electronics to monitor and control various aspects of machine operation, but the stitch formation remains mechanical. To automate the textile sewing operation, some sensing of whether the machine is performing properly is in order and necessary. Research of others such as Matthews & Little [2] and Murray [3] have worked to that end. Without such on-line monitoring for quality, one must rely on inspection further down the process stream to catch problems. In fast modern processing, a great amount of off-quality product may be produced before the inspection process finds and stops it.

UTOMATION of any process always requires that more sensing of the process be done electronically or mechanically in order to replace the observations made by a human operator. Too often engineers begin to consider replacing the actions of an operator by robotics or fixed automation without first considering the inspection duties inherently included in the job. These duties are frequently taken for granted and are 11. THREADMOTIONRATIO difficult or very expensive to replace with sensor technology. Early research done for the Defense Logistics Agency on the The vision of an operator, for example, can be replaced but single-needle lockstitch machine [4] showed that the time of the image analysis is often complex and difficult to replace. thread motion changed proportionally with the machine cycle Research by the author in this area was begun several time. In order to reduce the effect of speed as a source of error, years ago under a research contract with the Defense Logistics the normalized ratio TMR was calculated as follows: Agency [I] which is responsible for production of uniforms for the military services. The purpose of the research was to TMR = tthread motionltmachine cycle X 100%. promote automation in apparel manufacturing. The work was to develop new technologies which would permit automation TMR is actually the equivalent of the ratio of the average of sewing operations and thus make apparel production in the thread velocity divided by the average thread velocity during United States less labor intensive and therefore more competi- the intermittent motion. The thread is in motion from 15% to tive. The initial interest was in the common lockstitch machine 35% of the cycle time depending on the stitch type and the and the sewing of denim woven materials. More recently, particular thread in that stitch. the National Textile Center has funded research to expand The thread consumption (G) is calculated as follows: the knowledge and applicability of this new technology. The focus has shifted to other types of stitches and other materials = V(t)dt including knits. Stitch formation consists of the interlacing of one or more threads which penetrate multiple layers of textile fabric, where { t o : t l } is a thread motion interval and { t o : t 2 } is thereby holding them together. The simple single needle a machine cycle. If the velocity, vi is assumed constant over chainstitch requires only one thread supply while a double aby interval {t, : t b } needle chainstitch requires three thread packages. Other stitches which are combinations of the fundamental stitches

Paper PID 95-23, approved by the Textile, Fiber and Film Industry Committee of the IEEE Industry Applications Society for presentation at the 1995 IEEEDAS Annual Textile, Fiber and Film Industry Technical Conference, Charlotte, NC, May 2 4 . Manuscript released for publication May 14, 1995. The author is with the School of Textile & Fiber Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0295 USA. IEEE Log Number 9414388.

Let vint be the average velocity on the interval { t o : t l } and vaVgbe the average velocity over the machine cycle {to : t z } . These velocities are related to time and thread consumption

0093-9994/95$04.00 0 1995 IEEE

Authorized licensed use limited to: Hong Kong Polytechnic University. Downloaded on September 25, 2009 at 02:58 from IEEE Xplore. Restrictions apply.

Neural Network Predictions of Human Psychological of Clothing Sensory Comfort

Perceptions

A. S. W. WONG, Y. LI, AND P. K. W. YEUNG Institute

of Textiles and Clothing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

P. W. H. LEE Department of Psychiatry, University of Hong Kong, Pokfulam, Hong Kong

ABSTRACT

the

clothing

The objective of thisâ&#x20AC;&#x2122;paper is to investigate predictability of sensory comfort from psychological perceptions by using a feed-forward network in an artificial neural network (ANN) system. In order to achieve the objective, a series of wear trials is conducted in which ten sensory perceptions ( clammy, clingy, damp, sticky, heavy, prickly, scratchy, fit, breathable, and thermal) and overall clot hing comfort comfort are rated by twenty-two professional athletes in a controlled la ratory. They ( ) are asked to wear four different garments in each trial and rate the sensations above during a 90-minute exercising period. The scores are were input into five different eed-forward back-propagation neural network models, consisting of six different numbe rs of hidden and output transfer neurons. Results showing a good correlation betweenredicted and actual comfort ratings with a significance of 0:001for all five models ind icate overall < p comfort performance is predictable with neural networks, particularly models with log sigmoid hidden neurons and pure linear output neurons. Models with a single log sigmoid hidden layer with fifteen neurons or three hidden layers, each with ten log sigmoid hidden neurons, are able to produce better predictions than the other models for is particular data set in the study.

In order to survive in the

quickly changing, highly competitive clothing companies in textile and clothing industries are searching for competitive advantage by understanding and meeting consumer needs and desires. Various consumer research groups have reported market,

that modem consumers consider comfort one of the most important attributes in their purchase of textile and apparel products, so there is a need to develop a sound scientific understanding of the psychological perception of clothing comfort sensations. Up to now, there has been no one clear definition of comfort, since this subjective feeling differs from person to person, but a lot of researchers have investigated comfort over the past years. For example, LaMotte ( 1977) stated that physical comfort might be greatly influenced by tactile and thermal sensations arising from contact between skin and the immediate environment [3]. Slater ( 1986) defined comfort as &dquo;a pleasant state of physiological, psychological and physical harmony between a human being and the environment&dquo; 1101. Li ( 1986) defined comfort as a holistic concept, which is a state of multiple interactions of physical, physiological,

and

back-propagation

psychological factors [7]. owever. these definitions

only,, identify the factors influ cing human sensory perceptions;

the

these factors and detennined. yet 10 the past, many research rs of thermal and tactile comfort have used traditional tatistical methods such as cluster analysis and factor ana ysis [5, 6]. In earlier work ( Wong et al. [ 1 ? ]. we develo d a linear model based on traditional statistics to simulate huA~an psychological perception; of clothing senso comfort t!2j. We used stati~tical factor analysis to i ntify independent factors and Itheir relative contribution from ten sensory perceptions. We identified three jor factors of moisture. tactile. and thermal-fit comfo . and constructed a linear m(*l using these three factorâ&#x20AC;&#x2122; and their contributions as to predict overall co fort perceptions. Comparthe ing predictions with the ctual comfort ratings, we observed good agreement belween the two. indicating that overall clothing comfort be predicted by indi-

relationships

overall comfort have

not

weights

sensory perceptions. I The application of statistic

vid441 has

methods in this research number of limitations, however, including diffi-

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Neural Network Prediction of Human Psychological Perceptions of Fabric Hand C. L. HUI, T. W. LAU, Institute

of Textiles

and

Clothing,

The

AND

S. F. NG

Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong K. C. C. CHAN

Department of Computing,

The

Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong ABSTRACT

Fabric hand is commonly adopted for assessing fabric quality and prospective perforin a particular end use. In general, fabric hand is primarily assessed subjectively. Subjective assessments treat fabric hand as a psychological reaction obtained from the sense of touch, based on the experience and sensitivity of humans. It is very difficult to predict such psychological perceptions of hand based on fabric properties. In this paper, we identify reliable sensory fabric hand attributes with correlated attributes of fabric properties, and we attempt a novel approach for predicting sensory hand based on fabric properties using a resilient back-propagation neural network. In this study, we assess forty woven fabrics to determine twelve significant fabric properties and fourteen reliable attributes of sensory hand. Our proposed system performs at a very low mean square error after fine tuning. Five extra woven fabrics are used to show that the performance of such a prediction system closely agrees with subjective test results. Our proposed system can allow field practitioners to evaluate their fabrics more closely to match with customersâ&#x20AC;&#x2122; mance

expectations.

Fabric hand is a generic term for the tactile sensations associated with fabrics that influence consumer preferences [ 12]. It is basically a reflection of overall quality, consisting of a number of individual physical properties [24], and is the human response to touching, squeezing, rubbing, or otherwise handling a fabric [ 16]. It is commonly adopted for assessing fabric quality and prospective performance in a particular end use. In general, fabric hand can be assessed by subjective and objective methods. Subjective assessments treat fabric hand as a psychological reaction obtained by the sense of touch. It is a primary descriptive method based on the experience and sensitivity of human beings. Objective assessments attempt to predict fabric hand using instrumental data and sensory-instrumental relationships. Theoretical approaches to subjective fabric hand sensory assessments have recently aroused great interest in the area of clothing and textiles, and many researchers have looked for &dquo;world-famous&dquo; methodologies to transform subjective hand properties to objective measurements [38]. The motivation behind these works is due to

the different fabric sensory perceptions of individuals. Brand [2] is one of several researchers who commented on differences between vocabularies of experts and untrained judges of textile hand. Wauer [36] concluded that these differences are great enough to interfere with communications between experts and consumers. They may use the same adjective, say, &dquo;harsh,&dquo; to describe a hand that differs among individuals. Moreover, Brand [2] stated that, &dquo;Aesthetic concepts are basically peopleâ&#x20AC;&#x2122;s preferences and should be evaluated subjectively by people.&dquo; This differentiation has initiated much research focused on how to model subjective fabric hand objectively. Although many older techniques for evaluating fabric hand did not use standards or proper psychological methods, more recent approaches certainly do use standard scales and measures. For example, the Spectrum Method of Descriptive Hand Evaluation (Civille and Dus) [5] is based on a set of fifteen-point intensity scales for twenty-one different attributes of fabric hand. Each of these intensity scales is anchored at several points by specific fabric standards, i.e., physical references, so that

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375

European Journal of Operational Research 160 (2005) 501–514 www.elsevier.com/locate/dsw

Computing, Artiﬁcial Intelligence and Information Technology

Neural network forecasting for seasonal and trend time series G. Peter Zhang a

a,*

, Min Qi

Department of Management, J. Mack Robinson College of Business, Georgia State University, 35 Broad Street, NW, Atlanta, GA 30303, USA b Department of Economics, College of Business Administration, Kent State University, Kent, OH 44242, USA Received 19 October 2001; accepted 8 August 2003 Available online 18 November 2003

Abstract Neural networks have been widely used as a promising method for time series forecasting. However, limited empirical studies on seasonal time series forecasting with neural networks yield mixed results. While some find that neural networks are able to model seasonality directly and prior deseasonalization is not necessary, others conclude just the opposite. In this paper, we investigate the issue of how to effectively model time series with both seasonal and trend patterns. In particular, we study the effectiveness of data preprocessing, including deseasonalization and detrending, on neural network modeling and forecasting performance. Both simulation and real data are examined and results are compared to those obtained from the Box–Jenkins seasonal autoregressive integrated moving average models. We find that neural networks are not able to capture seasonal or trend variations effectively with the unpreprocessed raw data and either detrending or deseasonalization can dramatically reduce forecasting errors. Moreover, a combined detrending and deseasonalization is found to be the most effective data preprocessing approach. 2003 Elsevier B.V. All rights reserved. Keywords: Neural networks; Box–Jenkins method; Seasonality; Time series; Forecasting

1. Introduction Many business and economic time series exhibit seasonal and trend variations. Seasonality is a periodic and recurrent pattern caused by factors such as weather, holidays, repeating promotions, as well as the behavior of economic agents (Hylleberg, 1992). Although seasonal variations are perhaps the most signiﬁcant component in a seasonal time series, a stochastic trend is often ac*

Corresponding author. Tel.: +1-404-651-4065; fax: +1-404651-3498. E-mail address: gpzhang@gsu.edu (G.P. Zhang).

companied with the seasonal variations and can have a significant impact on various forecasting methods. A time series with trend is considered to be nonstationary and often needs to be made stationary before most modeling and forecasting processes take place. Accurate forecasting of seasonal and trend time series is very important for effective decisions in retail, marketing, production, inventory control, personnel, and many other business sectors (Makridakis and Wheelwright, 1987). Thus, how to model and forecast seasonal and trend time series has long been a major research topic that has significant practical implications.

Pergamon

Computers ind. EngngVol. 29, No. 1-4,pp. 513-517, 1995 CopyrightÂŠ 1995ElsevierScienceLtd Printed in GreatBritain.All rightsreserved 0360-8352/95 $9.50+ 0.00 0360-8352(95)00126-3

Minmax Earliness/Tardiness Scheduling in Identical Parallel Machine System Using Genetic Algorithms Runwei Cheng*

Mitsuo Gen t

Tatsumi Tozawa*

*Graduate School of Engineering Utsunomiya University, Utstinomiya 321, Japan t Department of Industrial and Systems Engineering Ashikaga Institute of Technology, Ashlkaga 326, Japan Abstract: In this paper, we address an earliness/tardiness scheduling problem in identical paral]el machine system with an objective of minimizing the maximum weighted absolute lateness. Genetic algorithms are applied to solve this problem. The performance of proposed procedure is compared with ex~ng heuristic procedure on randomly generated test problems. The results show that the prbposed approach performs well for this problem. Key words: Genetic algorithms, earllness/tardiness scheduling, identical parallel machine system and minmax optimization.

IntroduCtion

In this paper we discuss the application df genetic algorithms to earliness/tardiness scheduling problem in identical parallel machine system with an objective of minimizing the maximum weighted absolute lateness. This problem was firstly considered by Li and Cheng as follows [1]: there are a set of jobs associated with known processing times and weights, several parallel and identical machines, and a common due date that is not too early to constrain the scheduling decision. The objective is to find an optimed job schedule so as to minimize the maximum weighted absolute lateness. This kind of objective function is known as one of non-regular performance measures. In recent years, scheduling research involving non-regular performance measures has received much attention from practitioners as well as researchers to respond to the increasing competitive pressure in domestic and internationul market. Baker and Scudder [2] have presented a comprehensive survey of earliness/tardiness scheduling. Recent overviews on parallel machine scheduling research are given by Cheng and Sin [3]. There are two non-regular performance measures commonly used in earliness/tardiness scheduling: reinsure and minmax. A reinsure problem attempts to minimize the sum of weighted absolute deviation of job completion time about the due date, i.e., to reduce customers' aggregate disappointment; CAIE 29:1/4-1I

while a mlnmax problem attempts to mlnlmile the m~ximum weighted absolute deviation of job completion time about the due date, i.e., to reduce a customers maximum disappointment. Li and Cheng have shown that minmax scheduling problem is NP-complete even for single machine system. Due to the intrinsic difficulty of the problem, search methods based upon heuristics are most promising for solving such problem. Li and Cheng have proposed two greedy heuristic based procedures to solve this problem. In recent-years, a growing body of literature suggests the use of genetic algorithm as one of powerful heuristic search methods to solve combinatorial optimization problems [4]. Gupta at el. have solved the mlnsu.m scheduling problem in a single machine system using genetic algorithms [5]. Single machine schudeling problem just considers how to find out a best permutation of jobs with respect to someperformance measures. As we know that there are two essential issues to be dealt for all kind of multiple machine scheduling problems: â&#x20AC;˘ partition jobs to machines â&#x20AC;˘ sequence jobs for each machine Because genetic algorithms are very effective at performing global search for combinatorial optimization problems, in this paper, we investigate how to apply genetic algorithms to solve minmax multiple machines scheduling problem. An extended permutation representation is adopted as the coding scheme for multiple machines scheduling problem, crossover and mutation are defined to adjust job partition among machines and job permutation within each machine. The performance of proposed procedure is compared with Li and Cheng's greedy heuristic on randomly generated test problems. The results show that the proposed approach performs well for this problem.

P r o b l e m and A s s u m p t i o n s

We consider the following multiple machines scheduling problem: 513

ARTICLE IN PRESS

Int. J. Production Economics 114 (2008) 615–630 www.elsevier.com/locate/ijpe

238

Using a

Neural Network to Identify Fabric Defects in Cloth Inspection

CHUNG-FENG JEFFREY KUO, CHING-JENG LEE,

AND

Dynamic

CHENG-CHIH TSAI

Control and Simulation Laboratory, Department of Fiber & Polymer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, Republic of China

Intelligence

ABSTRACT In this research, an image system is used as a tool for dynamic inspection of fabrics, and the inspection sample is a piece of plain white fabric. The four defects are holes, oil stains, warp-lacking, and weft-lacking. The image treatment employs a high-resolution linear scan digital camera. Fabric images are acquired first, then the images are transferred to a computer for analysis. Finally, the data are adopted as input data for a neural network, which is obtained from readings after treating the images. In this system, there are three feedforward networks, an input layer, one hidden layer, and an output layer. Because it has the ability to cope with the nonlinear regression property, this method can reinforce the effects of image identification.

In recent years, the technology of image processing systems has been applied to inspections in the fabric industry, for instance, cotton cloth evaluations, fiber characteristics, structural characteristics of nonwovens, evaluations of carpet forming structures, and fabric defects, etc. Shin [8] used a texture-tuned mask method to identify defects, but oil stain results were poor. Wang [ 10] applied a skeleton method to identify surface defects but wasted too much time making the identifications. Ribolzi [6] made an optical electron analysis of warplacking and weft-lacking, and Konda [4] employed image analysis on woolen balls. In recent years, neural networks have been adopted for image analyses. Neubauer [5] used image segmentation technology in conjunction with a neural network in identifying fabric defects. Sanby [7] employed a line-scan digital camera to inspect lace. Vangheluwe [9] used image analysis and a neural network to measure set marks. Barrett [ 1 ] applied Fourier transformation and a neural network in a stitching system for on-line classification. Chen [3] applied a back propagation neural network to Fourier analysis to inspect fabric defects such as lack of yarns and oil stains. Actually, dynamic fabric inspection is more difficult than static fabric inspection because moving situations are more complicated, and the speed of computer processing is another critical issue. Bradshaw [2] reported that no computer has a detection efficiency greater than 60% when used for fabrics, and their use is therefore restricted to inspection for low to medium quality production. In this article, after acquiring an image from the media generated from a VC+ + program, the computer uses this image to process the

accumulations of image values from longitudinal and transverse directions in order to find the length, width, and gray level of a fabric defect. Not only can it find a defect in high-speed performance, it can also precisely calculate the length, width, and gray level of that defect.

System

Scheme

This research is intended to set up a dynamic inspection tum-key system for fast image acquisition with a linear scan digital camera. The essential factors considered here include whether or not the light-source conditions of the fabric images during the image-acquiring process by the line-scan digital camera are consistent. We also need to be aware of whether or not the cloth itself vibrates along with the conveyor belt, so we use a helium neon direct light source with strong brightness and stability. The stability of the entire module will increase with the best external environmental conditions. The camera DASA CL-C7-4096 is adapted for high speed linear scanning because it can pick up 7200 lines per second, and it can obtain high-precision images on a dynamic fabric. The major specifications are 7 X 7 ยกLm pixel pitch, 28.7 mm X 7 ยกLm lens diaphragm, and 7.2 kHZ maximum camera line rate. ANALYSIS

DEFECT FORMATION

The reasons for weft-lacking generally are too great a pick force strength and too high a tension [8]. Warplacking is due to poor original yarn, too short a warp route, or too high a tension. Because warp- or weft-

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ExpertSystemsWithApplications,Vol.9, No. 2, pp. 237-246,1995 CopyrightÂŠ 1995ElsevierScienceLtd Printedin the USA.All rightsreserved 0957-4174/95$9.50+ .00

Pergamon 0957-4174(94)00065-4

Expert System Support in the Textile Industry: End Product Production Planning Decisions E NELSON FORD Departmentof Management, Collegeof Business, Auburn University,Auburn, AL 36849, U.S.A.

JARICK RAGER Department of TextileEngineering, Collegeof Engineering,Auburn University,Auburn, AL 36849, U.S.A.

Abstract--The textile industry is slowly developing expert system applications to increase production,

improve quality, and reduce costs. Such systems are surfacing in a variety of areas throughout the textile manufacturing process. This paper describes an expert system developed to support an important decision scenario in the textile industry. The scenario concerns a sequence of production planning decisions necessary to produce a specific category of end product. This sequence is described as follows: given the decision to produce a particular type of end product, the appropriate fiber type is chosen; next, the appropriate yarn count group is chosen; next, the appropriate spinning system is chosen; and finally, the appropriate preparation method is chosen. Each decision in the sequence depends on the combination of decisions made in the preceding stages. The resulting system is described and its application is illustrated through the presentation of a sample consultation. The integration of the expert system into a broader environment for textile manufacturing decision support is also discussed.

1. I N T R O D U C T I O N

manufacturing process. Some of these are described briefly below.

EXPERT SVSa'EMS have emerged from research labs of leading universities and major corporations into business and industry for everyday use. The textile industry is slowly developing expert system applications to increase production, improve quality, and reduce costs. Expert systems function as intelligent assistants, serving any number of individuals needing help and guidance to solve problems and make sound decisions. Expert systems have the potential to increase machine efficiency, decrease maintenance down-time, and improve managerial decisions. The textile industry is beginning to realize the advantages of computerizing the perishable knowledge of the experts in the industry. Expert systems offer the potential of being the first practical method available to preserve the intellectual property of an organization (Demers, 1989).

1.1.1. North Carolina State University Expert System.

Expert system technology is used in the design of industrial fabrics and made-to-measure clothing. An expert system that aids in the structural design process for woven industrial fabrics was developed at North Carolina State University. Designers use the system to integrate knowledge concerning the structural properties of yams and fabrics with a customer's desired fabric characteristics. The system searches an industrial fabric data base to attempt to find a match that meets the customer's requirements. If an exact match is not found, the system will attempt to redesign a similar fabric found in the data base to meet the customer's requirements. If the system is still unable to make a match, the system will attempt to synthesize the entire design process to produce the fabric (Demers, 1989).

I.I. Sample Expert Systems in Textiles Expert systems developed for textile manufacturers are surfacing in a variety of areas throughout the textile

1.1.2. Clothing Design Expert System. A research team at the University of Maryland has developed the Clothing Design Expert System (CDES). CDES contains the Alteration Definition Tool (ADT) and the Pattern Requirement Language (PRL). These two tools provide a

Requests for reprints should be sent to F. Nelson Ford, Departmentof Management, College of Business, Auburn University, Auburn, AL 36849, U.S.A.. 237

ARTICLE IN PRESS

Int. J. Production Economics 99 (2006) 117–130 www.elsevier.com/locate/ijpe

Comparison of negotiation protocols in dynamic agent-based manufacturing systems Jihad Reaidya, , Pierre Massottea,b, Daniel Diepa a

Laboratoire de Ge´nie Informatique et Inge´nierie de Production, Ecole des Mines d’Ale`s, Parc Scientifique George Besse, 30035 Nıˆmes, France b IBM Academy of Technology, La Gaude, France Available online 28 January 2005

Abstract This paper proposes a negotiation methodology based on multi-agent system for heterarchical and complex manufacturing control systems. This approach has been selected to implement new paradigms based on ‘‘coopetition ¼ co-operation+competition’’ in order to improve the ‘‘production on demand’’ and reaction capabilities of distributed production systems related to the net-economy. Agents may represent products and resources of the system. The local scheduling and control functions in dynamic environments is addressed by a new negotiation protocol between agents based on the ‘‘request session’’ principle for cooperation and on the game theory approach for competition. r 2005 Elsevier B.V. All rights reserved. Keywords: Production and management control; ‘‘Co-opetition’’; Multi-agent systems; Negotiation protocol; Game theory

1. Introduction The application of multi-agent systems based on the concept of distributed artiﬁcial intelligence is considered as being one of the most promising control architectures for next-generation of complex production systems, speciﬁcally in a dynamic environment (failed resources, disturbances, etc.). Corresponding author. Tel.:+33 4 6638 7028.

E-mail addresses: reaidy@site-eerie.ema.fr (J. Reaidy), massotte@site-eerie.ema.fr (P. Massotte), diep@site-eerie.ema.fr (D. Diep).

In particular, very attractive solutions and efﬁcient issues are expected in the domain of local planning, and execution control, to improve the conventional supply chain management; here, the usual production system management consists, in a set of separate and heterogeneous application software packages, such as Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), Supervisory Control And Data Acquisition (SCADA), etc. These tools are not able to cover satisfactorily the constraints required by the new challenges of the economy such as networked enterprises, production on demand or mass

Building

Rule Set for the Fiber-to-Yarn Production Process Soft Computing Techniques S.

1 SETTE,

2 BOULLART,

AND

University of Ghent, Technologiepark 9,

L. VAN B-9052

by Means

1 LANGENHOVE

Zwijnaarde, Belgium

ABSTRACT An important aspect of the spinning process is the ability to predict the spinnability of yam and its resulting strength based on the fiber quality and machine settings. Currently available fiber-to-yarn models are limited to the so-called "black box" approach, generating an output (spinnability) without containing physical, interpretable information about the process itself. This paper presents a method to predict the spinnability and strength of a yam with a set of IF-THEN rules. The rule set is automatically generated using the available data by means of a new learning classifier system called a fuzzy efficiency-based classifier system (FECS), which enhances the original learning classifier algorithm of Goldberg [5] by defining several rule efficiencies and introducing them into the learning strategy of the system. Furthermore, FECS allows the introduction of continuous (fiber and yarn) parameters, which broaden the application fields considerably in contrast to discrete parameters alone. To this end, the generated rules are expanded to represent fuzzy classes with corresponding membership degrees toward each fiber-to-yarn data sample. Rule efficiencies and the reward mechanism are modified to account for the membership degree of each data sample. The paper demonstrates that the resulting prediction accuracy is good and, more importantly, also delivers additional qualitative information about the fiber-toyarn process behavior. The generated rule set allows almost 100% acceptable classification of yarn strength in three classes. The methodologies described in this paper are conveniently classified as "soft computing." a

One of the

important production processes in the textile is industry spinning. Starting with cotton fibers, yams are (usually) created on a rotor spinning machine. The spinnability of a fiber depends on its quality and the settings of the spinning machine. It would be very beneficial to be able to predict the spinnability and resulting strength of a yam starting from a certain quality and from machine settings. To this end, two totally different modeling approaches can be considered: so-called &dquo;white&dquo; modeling and &dquo;black box&dquo; modeling. In white modeling, the process is described by mathematical equations based on (theoretical) physical knowledge of the process. Extensive physical information about the process is in this case available through physical, chemical, or mechanical equations, giving the user a thorough insight into the operation of the process. However, due to the large input (and output) dimensions of the fiber1

Department of Textiles:

2 Department

e-mail : stefan.sette@rug.ac.be of Control Engineering & Automation: e-mail:

boullart@autoctrl.rug.ac.be

to-yarn process and their complex interactions,

no exact

mathematical model of a spinning machine is known to exist, nor is it likely that such a model will ever be constructed. A black box model, in contrast to white modeling, simply connects input parameters to the output without giving or containing any substantial physical information about the process itself. Black box models have been successfully constructed by Pynckels et al. to predict the spinnability [9] and characteristicsâ&#x20AC;&#x2122; [ 10] of a yam using neural networks with a backpropagation leaming rule. Apart from the lack of physical information, these models also have no fault indication or measure of uncertainty about the results. In this research, we will present a new modeling approach called the &dquo;efficiency-based classifier system&dquo; or Ecs to the fiber-to-yarn process by using an automated learning method to generate rules that allow us to predict the spinnability and strength of the yarn based on fiber quality and machine settings. This kind of approach could be called &dquo;grey modeling,&dquo; since not only is the relationship between input and output parameters estab-

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375

675

Applying Fuzzy Logic

and Neural Networks to Total Hand Evaluation of Knitted Fabrics

SHIN-WOONG PARK, YOUNG-GU HWANG, AND BOK-CHOON KANG Department of Textile Engineering, Inha University, Nam-Ku, 402-751, Inchon, South

Korea

SEONG-WON YEO Department of Electrical Engineering.

Inha

University, Nam-Ku, 402-751, Inchon,

South Korea

ABSTRACT This study of two new total hand simulating methods for knits uses fuzzy theory and neural networks. One method, a neural network system trained with a back-propagation algorithm, performs functional mapping between mechanical properties and the resulting total hand values of the fuzzy predicting method. The second method, a fuzzy-neural network system, uses the fuzzy membership function, weighted factor vector, and error back-propagation algorithm. The principal mechanical properties of stretchiness, bulkiness, flexibility, distortion, weight, and surface roughness of the knitted fabrics are correlated with experimentally determined Kawabata total hand values and fuzzy transformed overall hand values. Fuzzy and neural networks agree better with the subjective test results than the KES-FB system. The mechanical properties are fuzzified by fuzzy membership functions, then trained to predict the total hand value of outerwear knitted fabrics. In each case, the prediction error is less than the standard deviation of experimentation, and the optimum structure is investigated. These two systems, which use the Pascal programming language, produce objective ratings of outerwear knit fabrics.

previous papers [ 11-15], we have published data on by subjective hand assessment [21, so hand evalfuzzy prediction model for double weft knitted fabrics. uation systems are somewhat subjective and have sevTo replace traditional subjective fabric hand assessment, eral shortcomings when applied to other countries we have established an objective measure of quality and [9-11]. performance on the basis of low-stress mechanical propThere are, however, several problems in determining a

erties. Since the handle of fabrics obtained from touch and appearance is influenced by the mental and mechanical properties of the expert, it will be more meaningful to rate overall hand values with fuzzy theory and neural networks. To date, the KES-FB system is the criterion most commonly used to evaluate the total hand value of fabrics in textile research and industry [5]. With an objective test system such as the KES-FB for evaluating the mechanical properties of fabrics, it is possible to establish hand evaluation software, which helps to clarify objective ratings in mutual communications between different sectors in the industry about the quality of a fabric ( 16]. But primary hand expressions and the total hand value depend mainly on Japanese hand experts and cannot be correlated to other cultural backgrounds or to subjective factors. At present, the total hand of knitted fabrics is primarily evaluated

the total hand value of a knitted fabric, such as the difficulty of measuring, geographical climate, cultural factors, and application method [9-11. 17]. In order to overcome these shortcomings of the evaluation software in the KES-FB system, new theoretical methods such as a psychological model based on Stevenâ&#x20AC;&#x2122;s law [4], total handle evaluation based on the concept of Euclidean distance [8], an empirical model based on fuzzy theory [ 18], and variable clustering analysis methods [7] have been investigated. All of these are mainly objective statistical modeling methods exhibiting neither simulation, programing, nor automatic calculation of total hand value. In this paper, we describe the objective total hand evaluation systems for current outerwear knits developed using fuzzy logic and neural networks. We begin with a summary of the artificial neural network theory and the principle of the back-propagation algorithm.

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European Journal of Operational Research 145 (2003) 530–542 www.elsevier.com/locate/dsw

Discrete Optimization

Application of a mixed simulated annealing-genetic algorithm heuristic for the two-dimensional orthogonal packing problem T.W. Leung a, Chi Kin Chan b, Marvin D. Troutt b c

c,*

a Diocesan GirlsÕ School, Kowloon, Hong Kong Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong Department of Management and Information Systems, Kent State University, Kent, OH 44240, USA

Received 23 August 2000; accepted 10 January 2002

Abstract In this paper a pure meta-heuristic (genetic algorithm) and a mixed meta-heuristic (simulated annealing-genetic algorithm) were applied to two-dimensional orthogonal packing problems and the results were compared. The major motivation for applying a modiﬁed genetic algorithm is as an attempt to alleviate the problem of pre-mature convergence. We found that in the long run, the mixed heuristic produces better results; while the pure heuristic produces only ‘‘good’’ results, but produces them faster. Ó 2002 Elsevier Science B.V. All rights reserved. Keywords: Meta-heuristics; Mixed heuristics; Simulated annealing; Genetic algorithm; Two-dimensional orthogonal packing problem; Diﬀerence process strategy

1. Introduction The two-dimensional orthogonal packing problem consists of packing rectangular pieces of predetermined sizes into a large but ﬁnite rectangular plate (the stock plate), or equivalently, cutting small rectangular pieces from the large rectangular plate. We wish to ﬁnd ‘‘packing patterns’’ that minimize the unused area (trim loss). The problem has obvious relevancy to the textile,

Corresponding author. Tel.: +1-330-672-1145; fax: +1-330672-2953. E-mail address: mtroutt@bsa3.kent.edu (M.D. Troutt).

paper, and other industries, and in the threedimensional case, is related to the problem of packing boxes into a container. The problem has been formulated as an integer program. For that approach and variations, see Beasley (1985), Tsai (1993) or Christoﬁdes (1995). Recently, diﬀerent meta-heuristics have been applied to problems of this kind, for instance, see Parada et al. (1998), Lai and Chan (1997), Jakobs (1996), Glover et al. (1995), Dowsland (1993, 1996). For an introduction to meta-heuristics, see Osman and Laporte (1996). Based on the papers of Jakobs (1996), also Lai and Chan (1997), we have carried out extensive comparisons of these heuristics (Leung et al., 2000). An important building

Applied Mathematics and Computation 180 (2006) 111–127 www.elsevier.com/locate/amc

An immune algorithm approach to hybrid ﬂow shops scheduling with sequence-dependent setup times M. Zandieh *, S.M.T. Fatemi Ghomi *, S.M. Moattar Husseini

Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, Tehran, Iran

Abstract Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. This paper deals with the hybrid flow shop scheduling problems in which there are sequence dependent setup times, commonly known as the SDST hybrid flow shops. This type of production system is found in industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacture. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. An immune algorithm (IA) can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. This paper describes an immune algorithm approach to the scheduling of a SDST hybrid flow shop. An overview of the hybrid flow shops and the basic notions of an IA are first presented. Subsequently, the details of an IA approach are described and implemented. The results obtained are compared with those computed by Random Key Genetic Algorithm (RKGA) presented previously. From the results, it was established that IA outperformed RKGA. 2006 Elsevier Inc. All rights reserved. Keywords: Short-term scheduling; Hybrid flow shops; Sequence dependent setup times; Makespan; Heuristics; Immune algorithms

1. Introduction Several flow patterns can be encountered, depending on the number of operations required to process a job and on the number of available machines per operation. When a job requires only one operation for its completion, we characterize it as single-operation; otherwise, we call it multi-operation. In the latter case, the concept of routing may be introduced based on machines, we have single machine shop, flow shop, permutation flow shop, job shop, and open shop scheduling problems. When processing stages are considered instead of machines, we have parallel machine shop, hybrid flow shop, job shop with duplicate machines scheduling problems. The diagram in Fig. 1 illustrates schematically the relationships between the different machine environments [71]. *

Corresponding authors. E-mail addresses: z7725953@aut.ac.ir, mostafazzz@yahoo.com (M. Zandieh), fatemi@aut.ac.ir (S.M.T. Fatemi Ghomi), moattarh@ aut.ac.ir (S.M. Moattar Husseini). 0096-3003/$ - see front matter 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.amc.2005.11.136

Journal of the Chinese Institute of Industrial Engineers, Vol. 21, No. 1, pp. 59-67 (2004)

AN ELECTROMAGNETISM ALGORITHM OF NEURAL NETWORK ANALYSIS - AN APPLICATION TO TEXTILE RETAIL OPERATION Peitsang Wu* Department of Industrial Engineering and Management I-Shou University 1, Sec, 1, ShiueCheng RD., DaShu Shiang, Kaohsiung County, 840, Taiwan, R.O.C. Wen-Hung Yang Department of Industrial Engineering and Management Yuan-Ze University Nai-Chieh Wei Department of Industrial Engineering and Management I-Shou University

ABSTRACT This paper applies a heuristic algorithm, called the â&#x20AC;&#x153;Electromagnetism Algorithmâ&#x20AC;? (EM) [3], for neural network training. We develop a meta-model of the relationships between key inputs and performance measures of an apparel retail operations using neural network technology. This method simulates the electromagnetism theory of physics by considering each weight connection in a neural network as an electrical charge. Through the attraction and repulsion of the charges, weights move toward the optimality without being trapped into local optima like other algorithms such as genetic algorithm and gradient descent method. The computation results show that the EM algorithm not only converges much faster than those of genetic algorithms and back propagation algorithms in terms of CPU time but also saves more memories than those in genetic algorithms and back propagation algorithms. Keywords: neural networks, electromagnetism algorithms, quick response, textile manufacturing, retail operations

1. INTRODUCTION The textile industry is extremely competitive internationally. Due to the low cost of foreign labor (e.g. China and Southeast Asia competitors), the textile industry has been rapidly losing its market share to overseas competitors. Because the industry is labor intensive, many jobs are threatened. In an effort to curtail the loss of market share to overseas competitors, the development of quick response (QR) methodologies for apparel was undertaken in 1984 under the auspices of Crafted With Pride, Inc. (CWP), U. S. A. [7]. Nuttle et al. [14] have developed a simulation model of an apparel retail store in order to obtain quantitative comparisons of QR and traditional retailing procedures for seasonal apparel in a wide variety of settings and at negligible cost (for simulation). The retail model represents part of an ongoing research effort to model the textile apparel *

Corresponding author: pwu@isu.edu.tw

retail chain [10]. The retail simulation model allows rapid cost/benefit studies of specific retailing situations, permits the buyer to play out specific scenarios, and provides the retail executive with a tool for the development of QR procedures. Experimentation with the model [8,14] has shown the clear advantage of QR over traditional retailing practice, as well as the limitation of QR in terms of selling season length, sales/SKU (stock-keeping-unit), etc. Additional results with the retail model linked to an apparel manufacturing model can be found in [9]. In this paper, a neural network for textile retail operations is presented. It is capable of capturing the essential features of the retail simulation model in multidimensional, mathematical relationships between performance (e.g. service level and lost sales) and key decision parameters (e.g. SKU mix and season length). The simulation model is used to generate the training data. Once trained, the neural network is able to

Knowledge and Information Systems (2002) 4: 257â&#x20AC;&#x201C;282 Ownership and Copyright c 2002 Springer-Verlag London Ltd.

Agents in E-Commerce: State of the Art Minghua He and Ho-fung Leung Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, PR China

Abstract. This paper surveys the state of the art of agent-mediated electronic commerce (ecommerce), especially in business-to-consumer (B2C) e-commerce and business-to-business (B2B) e-commerce. From the consumer buying behaviour perspective, the roles of agents in B2C e-commerce are: product brokering, merchant brokering, and negotiation. The applications of agents in B2B e-commerce are mainly in supply chain management. Mobile agents, evolutionary agents, and data-mining agents are some special techniques which can be applied in agent-mediated e-commerce. In addition, some technologies for implementation are briefly reviewed. Finally, we conclude this paper by discussions on the future directions of agent-mediated e-commerce. Keywords: Agent; Auction; Contract; Electronic commerce; Negotiation; Supply chain

1. Introduction Today, agents and electronic commerce (e-commerce) are among the most important and exciting areas of research and development in information technology. Combining these two fields offers lucrative opportunities both for business to conduct transactions on-line and for developers of tools to facilitate this trend (Tolle and Chen, 2000). This paper tries to draw a picture of the state of the art of agents in e-commerce, especially in two popular branches in the current research field: business-to-consumer (B2C) and business-to-business (B2B) e-commerce.

1.1. Agents and Multi-Agent Systems An agent is a hardware or (more usually) software entity with (some of) the following characteristics (Jennings et al., 1998; Ferber, 1999; Shoham, 1999): Received 14 September 2000 Revised 13 January 2001 Accepted 27 February 2001

Proceedings of the World Congress on Engineering 2007 Vol I WCE 2007, July 2 - 4, 2007, London, U.K.

A Neural Network Approach to Objective Evaluation of Seam Pucker K.L. MAK, WEI LI

Abstract—Seam pucker grade is one of the most important quality parameters in garments manufacturing industry. At present, seam pucker is usually evaluated by human inspectors, which is subjective, unreliable and time-consuming. Instead of subjective evaluation, this paper presents an objective method by using image analysis and pattern recognition. The evaluation system consists of image acquisition, image normalization, feature extraction and self organizing map classifier. Textural features of seam puckers are studied with a widely used statistical method, the co-occurrence matrix approach. The grades of seam puckers can be obtained from the trained self organizing map classifier and the results are very promising. Index Terms—Classification, Seam puckers, Self organizing map, Fabrics.

I. INTRODUCTION Nowadays, garment manufacturing industries are faced with increased pressure to become more competitive by increasing yield whilst reducing costs. The ability to compete mainly depends on productivity and quality. With the advances in electronic technologies, much can be done to improve productivity and quality by using automation as an integral part of manufacturing systems. However, automated vision-based inspection of textile products has been developing at a relative slow pace, and has not been widely studied in the research literature. Seam pucker is defined as the ridges, wrinkles, and corrugations running along the seam line of garments, and has been regarded as one of the most serious faults in garment manufacturing. It is usually caused by improper selection of sewing parameters and material properties, which results in unevenness on fabrics being stitched together, thus impairing their aesthetic values. In severe cases, seam pucker could appear like a wave front, originating from the seam, and extending to the entire piece of garment, e.g., when the seam is the center ridge linking the two pieces of fabrics in the back of a man’s suit. In less severe cases, the wave formation is less pronounced, but nevertheless discernible. Indeed, garments

Manuscript received March 22, 2007. K. L. Mak is Professor at the Dept. of Industrial and Manufacturing Systems Engineering (IMSE), the University of Hong Kong. (phone: 852-28592582; e-mail: makkl@hkucc.hku.hk). WEI LI is a PhD student at the Dept. of IMSE, HKU (e-mail: liwei@hkusua.hku.hk).

ISBN:978-988-98671-5-7

exhibiting pronounced seam pucker are certainly unwelcome by customers. It has been well recognized that elimination of seam pucker entirely is almost impossible, and the common practice is to accept a small amount of pucker as normal. Hence, it is essential to be able to grade puckered seams as objectively as possible. For this purpose, a set of photographic standards (Fig. 1) has been produced by the American Association of Textiles Chemists and Colorists (AATCC) which shows five standard classes in descending order of severity, from class 5 (no pucker) to class 1 (the most severe pucker). Using this method, observers compare each seam sample with the standard photographs and classify the sample as similar in pucker severity to one of the standard classes. However, this human inspection process is known to be subjective, unreliable and inconsistent. Since quality control plays a prominent role in garment manufacturing, the ability to evaluate seam puckers and to solve the seam pucker problem in the manufacturing process becomes vital. An objective method to evaluate seam pucker is therefore highly desired.

Fig. 1. Photographic standards for subjective pucker inspection by the AATCC method [7]. Although some research [1-7] has been conducted over the years to evaluate seam puckers objectively, the economical and accurate method is still absent. In this paper, an objective evaluation method based on the technique of artificial neural networks is presented to grade seam puckers with high accuracy.

WCE 2007

int. j. prod. res., 1998, vol. 36, no. 9, 2543± 2551

A genetic algorithm for scheduling job families on a single machine with arbitrary earliness/tardiness penalties and an unrestricted common due date S. WEBSTER² *, P. D. JOG³ and A. GUPTA§ We propose and investigate a genetic algorithm for scheduling jobs about an unrestricted common due date on a single machine. The objective is to minimize total earliness and tardiness cost where early and tardy penalty rates are allowed to be arbitrary for each job. Jobs are classi® ed into families and a family setup time is required between jobs from two di erent families. Results from a computational study are promising with close to optimal solutions obtained rather easily and quickly.

Introduction

The study of earliness and tardiness penalties in scheduling models has recently generated widespread interest among researchers. Past research on scheduling has concentrated mainly on regular performance measures in which the objective function is non-decreasing in job completion times. Some commonly used regular measures include mean ¯ owtime, maximum lateness and mean tardiness. However, practitioners are increasingly adopting concepts such as just-in-time and zero inventory to respond to competitive pressures. These concepts stress that earliness, as well as tardiness, should be discouraged, and an ideal schedule is one where each job completes on its assigned due date. Consequently, non-regular performance criteria such as the completion time variance or sum of earliness/tardiness (E/T) penalties are more appropriate in these settings. The importance of meeting due dates is well understood in practice. Traditionally, however, the due date has been assumed to be an external variable, outside the control of the job shop manager. Conway (1965 ) was the ® rst to formally introduce due date selection as part of the scheduling problem. This issue of due date as a decision variable has since received considerable attention. Overviews of this line of research can be found in Baker (1984 ), Ragatz and Mabert (1984 ), Cheng and Gupta (1989 ), Kanet and Christy (1989), Baker and Scudder (1990 ), and Christy and Kanet (1990 ). Most of the literature on E/T problems addresses single machine models where the set of jobs to be scheduled is known in advance and all jobs are available for processing. A number of authors have studied a model where all jobs share a common due date, but the due date is allowed to be an unrestricted decision variable. The reader is referred to Cheng and Gupta (1989), and Baker and Scudder (1990) for Revision received 1997.

² School of Management, Syracuse University, Syracuse, NY, USA. Motorola, Arlington Heights, IL, USA. § Andersen Consulting, Northbrook, IL, USA. * To whom correspondence should be addressed. 0020± 7543/98 $12. 00

1998 Taylor & Francis Ltd.

European Journal of Operational Research 169 (2006) 781–800 www.elsevier.com/locate/ejor

A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility Rubeń Ruiz *, Concepcioń Maroto Departamento de Estadı´stica e Investigacioń Operativa Aplicadas y Calidad, Universidad Politećnica de Valencia, Camino de Vera S/N, 46021 Valencia, Spain Received 30 September 2003; accepted 28 June 2004 Available online 14 March 2005

Abstract After 50 years of research in the field of flowshop scheduling problems the scientific community still observes a noticeable gap between the theory and the practice of scheduling. In this paper we aim to provide a metaheuristic, in the form of a genetic algorithm, to a complex generalized flowshop scheduling problem that results from the addition of unrelated parallel machines at each stage, sequence dependent setup times and machine eligibility. Such a problem is common in the production of textiles and ceramic tiles. The proposed algorithm incorporates new characteristics and four new crossover operators. We show an extensive calibration of the different parameters and operators by means of experimental designs. To evaluate the proposed algorithm we present several adaptations of other well-known and recent metaheuristics to the problem and conduct several experiments with a set of 1320 random instances as well as with real data taken from companies of the ceramic tile manufacturing sector. The results indicate that the proposed algorithm is more effective than all other adaptations. 2005 Elsevier B.V. All rights reserved. Keywords: Scheduling; Hybrid flowshop; Setup times; Genetic algorithm

1. Introduction The area of ﬂowshop scheduling has been a very active ﬁeld of research during the last 50 years since Johnson (1954) seminal work. This research includes literally hundreds of papers in exact tech*

Corresponding author. Tel.: +34 96 387 70 07x74946; fax: +34 96 387 74 99. E-mail address: rruiz@eio.upv.es (R. Ruiz).

niques and also heuristic and metaheuristic algorithms for ﬂowshop scheduling problems and some of its variants. However, the reality of the production systems is more complicated and there is a noticeable gap between the theory and the application of the existing methods. Graves (1981) pointed out this problem and proposed several research directions to aid in bridging this gap. Other studies, like the ones in Ledbetter and Cox (1977) and Ford et al. (1987), show that there is

IEEE TRANSACTIONSON FUZZY SYSTEMS, VOL. 2. NO. 3, AUGUST 1994

Papers A Fuzzy Neural Network and its Application to Pattern Recognition Hon Keung Kwan, Senior Member, IEEE and Yaling Cai, Student Member, IEEE

Abstract-In this paper, we define four types of fuzzy neurons and propose the structure of a four-layer feedforward fuzzy neural network (FNN) and its associated learning algorithm. The proposed four-layer FNN performs well when used to recognize shifted and distorted training patterns. When an input pattern is provided, the network first fuzzifies this pattern and then computes the similarities of this pattern to all of the learned patterns. The network then reaches a conclusion by selecting the learned pattern with the highest similarity and gives a nonfuzzy output. The 26 English alphabets and the 10 Arabic numerals, each represented by 16x 16 pixels, were used as original training patterns. In the simulation experiments, the original 36 exemplar patterns were shifted in eight directions by 1 pixel (6.25% to 8.84%) and 2 pixels (12.5‘h to 17.68% ). After the FNN has been trained by the 36 exemplar patterns, the FNN can recall all of the learned patterns with recognition rate. It can also recognize patterns shifted by 1 pixel in eight directions with loo%, recognition rate and patterns shifted by 2 pixels in eight directions with an average recognition rate of 92.01%. After the FNN has been trained by the 36 exemplar patterns and 72 shifted patterns, it can recognize patterns shifted by 1 pixel with recognition rate and patterns shifted by 2 pixels with an average recognition rate of 98.61%. We have also tested the FNN with 10 kinds of distorted patterns for each of the 36 exemplars. The FNN can recognize all of the distorted patterns with 100% recognition rate. The proposed FNN can also be adapted for applications in some other pattern recognition problems.

I. INTRODUCTION

NEURAL NETWORK (NN) has a massively parallel structure which is composed of many processing elements connected to each other through weights [ 11-[3]. Neural networks (NN’s) are built after biological neural systems. A NN stores patterns with distributed coding and is a trainable nonlinear dynamic system. A NN has a faster response and a higher performance than those of a sequential digital computer in emulating the capabilities of the human brain. Recently, NN’s have been used in pattem recognition problems, especially where input patterns are shifted in position and scale-changed. Fukushima et al. [4], [SI have presented the Neocognitron, which is insensitive to translation and deformation of input patterns, and used it to recognize handprinted characters. However, the Neocognitron is complex Manuscript received July 2. 1992; revised February 14, 1994; accepted July 5 . 1993. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada The authors are with the Department of Electrical Engineering. University of Windsor. Windsor. Ontario, Canada N9B 3P4. IEEE Log Number 9401794.

and needs many cells. Carpenter and Grossberg [6] have proposed a self-organizing system which can classify patterns by adaptive resonance theory. However, a lot of internal exemplars including noise patterns are formed in the network. Martin and Pittman [7] have used a backpropagation (BP) learning network to recognize hand-printed letters and digits. Guyon et al. [8] have designed a system for on-line recognition of handwritten characters for a touch terminal using a timedelay neural network and the BP algorithm. Fukumi et al. 191 have proposed a neural pattem recognition system trained by the BP algorithm which can be used to recognize rotated patterns. In [7]-[9], the major problem lies in the lengthy training time of the BP algorithm which does not exist in the proposed fuzzy neural network. Perantonis and Lisboa 1101 have constructed a pattern recognition system which is invariant to the translation, rotation, and scale of an input pattern by high-order neural networks. However, the number of weights in such a network increases greatly with the order of the network. On the other hand, fuzzy logic [11]-[15] is a powerful tool for modeling human thinking and perception. Instead of bivalent propositions, fuzzy systems reason with multivalued sets. Fuzzy systems store rules and estimate sampled functions from linguistic input to linguistic output. It is believed that the effectiveness of the human brain is not only from precise cognition, but also from fuzzy concept, fuzzy judgment and fuzzy reasoning. Dealing with uncertainty is a common problem in pattern recognition. Fuzzy set theory has proved itself to be of significant importance in pattem recognition problems [ 121-[19]. Fuzzy methods are particularly useful when it is not reasonable to assume class density functions and statistical independence of features. Some work have been carried out on fuzzy neural systems for pattern recognition. Kosko [14] has proposed a Fuzzy Associate Memory (FAM) which defined mappings between fuzzy sets. FAM used fuzzy matrices instead of fuzzy neurons to represent fuzzy associations. Yamakawa and Tomoda [ 171 have described a simple fuzzy neuron model and used in a neural network for application in character recognition problems. However, they did not describe the specific leaming algorithm for this network. Takagi et ul. [ 181 have constructed a structured neural network using the structure of fuzzy inference rules. This structured NN has better performance than ordinary NN’s when used in pattem recognition problems. However, it is complicated to train this NN as it is composed of

10634706/94$04.00 0 1994 IEEE

Authorized licensed use limited to: Hong Kong Polytechnic University. Downloaded on September 25, 2009 at 01:00 from IEEE Xplore. Restrictions apply.

675

Applying Fuzzy Logic

and Neural Networks to Total Hand Evaluation of Knitted Fabrics

SHIN-WOONG PARK, YOUNG-GU HWANG, AND BOK-CHOON KANG Department of Textile Engineering, Inha University, Nam-Ku, 402-751, Inchon, South

Korea

SEONG-WON YEO Department of Electrical Engineering.

Inha

University, Nam-Ku, 402-751, Inchon,

South Korea

Downloaded from http://trj.sagepub.com at The Hong Kong Polytechnic University on September 24, 2009

Mathematical and Computer Modelling 46 (2007) 1419–1433 www.elsevier.com/locate/mcm

Two storage inventory model with fuzzy deterioration over a random planning horizon Arindam Roy a,∗ , Manas Kumar Maiti b , Samarjit Kar a , Manoranjan Maiti c a Department of Engineering Science, Haldia Institute of Technology, Haldia, Purba-Medinipur, W.B, Pin-721657, India b Department of Mathematics, Mahishadal Raj College, Mahishadal, Purba-Medinipur, W.B, Pin-721628, India c Department of Applied Mathematics, Vidyasagar University, Paschim-Medinipur, W.B, Pin-721102, India

Received 14 June 2006; received in revised form 25 January 2007; accepted 7 February 2007

Abstract An inventory model for a deteriorating item with stock dependent demand is developed under two storage facilities over a random planning horizon, which is assumed to follow exponential distribution with known parameter. For crisp deterioration rate, the expected profit is derived and maximized via genetic algorithm (GA). On the other hand, when deterioration rate is imprecise then optimistic/pessimistic equivalent of fuzzy objective function is obtained using possibility/necessity measure of fuzzy event. Fuzzy simulation process is proposed to maximize the optimistic/pessimistic return and finally fuzzy simulation-based GA is developed to solve the model. The models are illustrated with some numerical data. Sensitivity analyses on expected profit function with respect to distribution parameter λ and confidence levels α1 and α2 are also presented. c 2007 Elsevier Ltd. All rights reserved. Keywords: Fuzzy deterioration rate; Stochastic planning horizon; Possibility; Necessity

1. Introduction Classical inventory models are usually developed over infinite planning horizon. According to Gurnani [8] and Chung and Kim [4], the assumption of an infinite planning horizon is not realistic due to several reasons such as variation of inventory costs, changes in product specifications and designs, technological changes, etc. Moreover, for seasonal products like fruits, vegetables, warm garments, etc., the business period is not infinite. There are some models (cf. [5,2,12], etc.) in which time horizon has been considered as finite. For seasonal products, the planning horizon varies over years and may be considered as stochastic with a distribution. Moon and Yun [17] developed an EOQ model with a random planning horizon. Recently Moon and Lee [16] presented an EOQ model under inflation and discounting with a random product life cycle. Again for seasonal products deterioration is a real life phenomenon and the rate of deterioration is normally imprecise in nature [6]. Though a considerable number of research papers has been published for deteriorating items [3,1,7] none has considered planning horizon of such products as random in nature especially when deterioration is imprecise. ∗ Corresponding author. Tel.: +91 03224 52900; fax: +91 03224 52800.

Computers and Structures 84 (2006) 585–603 www.elsevier.com/locate/compstruc

Time-dependent reliability of textile-strengthened RC structures under consideration of fuzzy randomness Bernd Mo¨ller *, Michael Beer, Wolfgang Graf, Jan-Uwe Sickert Institute of Statics and Dynamics of Structures, Department of Civil Engineering, Technische Universita¨t Dresden, D-01062 Dresden, Germany Received 9 September 2004; accepted 25 October 2005 Available online 18 January 2006

Abstract The reliability of civil engineering structures is time-dependent. By means of strengthening it is possible to improve the load-bearing capacity and serviceability of structures and simultaneously to increase structural reliability. In this paper, we focus on the time-dependent reliability assessment of RC structures strengthened by textile-reinforced fine-grade concrete layers. The paper starts with a short introduction concerning textile strengthening of RC structures and the underlying mechanical model. The uncertain material parameters of textile-strengthened structures are then investigated. The uncertain parameters are quantified as fuzzy variables or fuzzy random variables. In order to take account of the latter in the assessment of the time-dependent reliability a new fuzzy probabilistic safety concept is presented. The fuzzy adaptive importance sampling (FAIS) method is introduced. The algorithm is demonstrated with an example. The uncertainty of the input parameters is comprehensively reflected in the uncertainty of the computed fuzzy reliability index. The assessment of the uncertain results is discussed. 2005 Elsevier Ltd. All rights reserved. Keywords: Structural safety; Uncertainty; Fuzzy randomness; Fuzzy probability; Imprecise probability; Fuzzy adaptive importance sampling (FAIS); Textile-strengthened structures; Textile concrete

1. Data uncertainty and textile-strengthened RC structures 1.1. Textile strengthening of RC structures In order to strengthen damaged RC structures textilereinforced fine-grade concrete layers are applied to the surface. The textile reinforcement of these layers consists of filament yarns (rovings) connected together by stitching yarn (see Fig. 1). Each roving is comprised of a large number of single filaments. The textile reinforcement may consist of different fiber materials, e.g. alkali resistant glass (ARG) or carbon. The strengthening of reinforced concrete (aged concrete) with textile-reinforced fine-grade concrete (textile concrete) results in a composite (Fig. 2). The load-bearing behavior *

Corresponding author. Tel.: +49 351 463 34386; fax: +49 351 463 37086. E-mail address: moeller@rcs.urz.tu-dresden.de (B. Mo¨ller). 0045-7949/$ - see front matter 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.compstruc.2005.10.006

of this composite is determined by the material properties of the steel-reinforced concrete, the textile-reinforced concrete, and the bond between them. An extended layered model with specific kinematics is used to describe the load-bearing behavior of RC constructions with textile strengthening [1]. This model is referred to as multi-reference-plane model (MRM). The MRM consists of concrete layers and steel reinforcement layers of the aged construction, the strengthening layers comprised of the inhomogeneous material textile concrete and of the interface layers (Fig. 3). This multi-layer continuum has the following kinematic features. Because the modification of the concrete layer thickness is very slight and can be neglected, e33 = 0 holds. Furthermore, the transverse shear stresses in the concrete layers have no significant influence on the deformation, e13 and e23 can be set to zero. The deformation state of the concrete layers may thus be described by Kirchhoff kinematics. The independent degrees of freedom are

Journal of Hazardous Materials B137 (2006) 1788–1795

The use of artificial neural networks (ANN) for modeling of decolorization of textile dye solution containing C. I. Basic Yellow 28 by electrocoagulation process N. Daneshvar ∗ , A.R. Khataee 1 , N. Djafarzadeh 1 Water and Wastewater Treatment Research Laboratory, Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran Received 5 April 2006; received in revised form 7 May 2006; accepted 8 May 2006 Available online 22 May 2006

Abstract In this paper, electrocoagulation has been used for removal of color from solution containing C. I. Basic Yellow 28. The effect of operational parameters such as current density, initial pH of the solution, time of electrolysis, initial dye concentration, distance between the electrodes, retention time and solution conductivity were studied in an attempt to reach higher removal efficiency. Our results showed that the increase of current density up to 80 A m−2 enhanced the color removal efficiency, the electrolysis time was 7 min and the range of pH was determined 5–8. It was found that for achieving a high color removal percent, the conductivity of the solution and the initial concentration of dye should be 10 mS cm−1 and 50 mg l−1 , respectively. An artificial neural networks (ANN) model was developed to predict the performance of decolorization efficiency by EC process based on experimental data obtained in a laboratory batch reactor. A comparison between the predicted results of the designed ANN model and experimental data was also conducted. The model can describe the color removal percent under different conditions. © 2006 Elsevier B.V. All rights reserved. Keywords: Artificial neural networks; Electrocoagulation; Modeling; Decolorization; C. I. Basic Yellow 28

1. Introduction Many industries such as plastics, paper, textile and cosmetics use dyes in order to color their products. These molecules are common water pollutants and they may be frequently found in trace quantities in industrial wastewaters. Textile plants, particularly those involved in finishing processes, are major water consumers and the source of considerable pollution. The disposal of these colored wastewaters poses a major problem for the industry as well as a threat to the environment. There are many processes to remove dyes from colored effluents such as adsorption, precipitation, chemical degradation, photodegradation, biodegradation, chemical coagulation and electrocoagulation [1–3]. Adsorption and precipitation processes are very time-consuming and costly with low efficiency. Chemical degra∗

Corresponding author. Tel.: +98 411 3393146; fax: +98 411 3393038. E-mail addresses: nezam daneshvar@yahoo.com (N. Daneshvar), ar khataee@yahoo.com (A.R. Khataee), n.jafarzadeh@gmail.com (N. Djafarzadeh). 1 Tel.: +98 411 3393165; fax: +98 411 3393038. 0304-3894/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.jhazmat.2006.05.042

dation by oxidative agents such as chlorine is the most important and effective methods, but it produces some very toxic products such as organochlorine compounds [1]. Photooxidation by UV/H2 O2 or UV/TiO2 needs additional chemicals and therefore causes a secondary pollution. Although biodegradation process is cheaper than other methods, it is less effective because of the toxicity of dyes that has an inhibiting effect on the bacterial development [2,3]. Hence, electrocoagulation (EC) as an electrochemical method was developed to overcome the drawbacks of conventional water and wastewater treatment technologies. EC process provides a simple, reliable and cost-effective method for the treatment of wastewater without any need for additional chemicals, and thus the secondary pollution. It also reduces the amount of sludge, which needs to be disposed [3–5]. EC technique uses a direct current source between metal electrodes immersed in polluted water. The electrical current causes the dissolution of metal electrodes commonly iron or aluminum into wastewater. The metal ions, at an appropriate pH, can form wide ranges of coagulated species and metal hydroxides that destabilize and aggregate the suspended particles or precipitate

The current issue and full text archive of this journal is available at www.emeraldinsight.com/0955-6222.htm

The single-row machine layout problem in apparel manufacturing by hierarchical order-based genetic algorithm

Single-row machine layout problem

Miao-Tzu Lin

Received 2 February 2008 Revised 28 May 2008 Accepted 28 May 2008

Department of Fashion Design and Management, Tainan University of Technology, Taiwan

Abstract Purpose – The purpose of this paper is to address the topic of minimizing the moving distance among cutting pieces during apparel manufacturing. Change machine layout is often required for small quantity and diversified orders in the apparel manufacturing industry. The paper seeks to describe a hierarchical order-based genetic algorithm to quickly identify an optimal layout that effectively shortens the distance among cutting pieces, thereby reducing production costs. Design/methodology/approach – The chromosomes of the hierarchical order-based genetic algorithm consist of the control genes and the modular genes to acquire the parametric genes, a precedence matrix and a from-to matrix to calculate the distance among cutting pieces. Findings – The paper used men’s shirt manufacturing as an example for testing the results of a U-shaped single-row machine layout to quickly determine an optimal layout and improve effectiveness by approximately 21.4 per cent. Research limitations/implications – The manufacturing order is known. The machine layout is in a linear single-row flow path. The machine layout of the sewing department is independently planned. Originality/value – The advantage of the hierarchical order-based genetic algorithm proposed is that it is able to make random and global searches to determine the optimal solution for multiple sites simultaneously and also to increase algorithm efficiency and shorten the distance among cutting pieces effectively, according to manufacturing order and limited conditions. Keywords Garment industry, Textile machinery and accessories, Programming and algorithm theory, Process planning Paper type Research paper

Introduction Good machine layout and shorten moving distance among materials are important for reducing production costs. Tompkins et al. (1996) indicated that moving non-value added material often takes up 20-50 per cent of the total manufacturing costs, and an efficient layout can save 10-30 per cent of the total manufacturing costs implying that an optimal layout can improve manufacturing schedules and therefore efficiency. Apparel manufacturing involves small quantities of diversified items that often require changes in machine layouts according to different materials, specifications, and manufacturing processes and methods. If the machine layout is able to be re-arranged quickly, then the change time, labor required, and moving distance can be reduced.

International Journal of Clothing Science and Technology Vol. 21 No. 1, 2009 pp. 31-43 q Emerald Group Publishing Limited 0955-6222 DOI 10.1108/09556220910923737

The principles of intelligent textile and garment manufacturing systems George Stylios Assembly Automation; 1996; 16, 3; ABI/INFORM Global pg. 40

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

Expert Systems with Applications 36 (2009) 3845–3856

Contents lists available at ScienceDirect

Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa

Stitching defect detection and classiﬁcation using wavelet transform and BP neural network W.K. Wong a,*, C.W.M. Yuen a, D.D. Fan a, L.K. Chan a, E.H.K. Fung b a b

Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong

a r t i c l e

i n f o

Keywords: Stitching defect Image segmentation Defect classiﬁcation Wavelet transform Quadrant mean ﬁlter Neural network

a b s t r a c t In the textile and clothing industry, much research has been conducted on fabric defect automatic detection and classification. However, little research has been done to evaluate specifically the stitching defects of a garment. In this study, a stitching detection and classification technique is presented, which combines the improved thresholding method based on the wavelet transform with the back propagation (BP) neural network. The smooth subimage at a certain resolution level using the pyramid wavelet transform was obtained. The study uses the direct thresholding method, which is based on wavelet transform smooth subimages from the use of a quadrant mean filtering method, to attenuate the texture background and preserve the anomalies. The images are then segmented by thresholding processing and noise filtering. Nine characteristic variables based on the spectral measure of the binary images were collected and input into a BP neural network to classify the sample images. The classification results demonstrate that the proposed method can identify five classes of stitching defects effectively. Comparisons of the proposed new direct thresholding method with the direct thresholding method based on the wavelet transform detailed subimages and the automatic band selection for wavelet reconstruction method were made and the experimental results show that the proposed method outperforms the other two approaches. Ó 2008 Published by Elsevier Ltd.

1. Introduction Detection of defects plays an important role in the automated inspection of fabrics and garment products. Quality inspection of garment manufacturing still relies heavily on trained and experienced personnel checking semi-finished and finished garments visually. However, manual inspection imposes limitations on identifying defects in terms of accuracy, consistency and efficiency, as workers are subject to fatigue or boredom and thus inaccurate, uncertain and biased inspection results are often produced. As a result, garment inspection is highly prone to errors and it allows defects to go undetected. To tackle these problems, it is necessary to set up an advanced inspection system for garment checking that can decrease or even eliminate the demand for manual inspection and increase product quality. In automated inspection, it is necessary to solve the problem of detecting small defects that locally break the homogeneity of a texture pattern and to classify all different kinds of defects. Various techniques have been developed for fabric defect inspection. Most of the defect detection algorithms tackling the problem use Gauss-

* Corresponding author. E-mail address: tcwongca@inet.polyu.edu.hk (W.K. Wong). 0957-4174/$ - see front matter Ó 2008 Published by Elsevier Ltd. doi:10.1016/j.eswa.2008.02.066

ian Markov random field, the Fourier transform, the Gabor filters or the wavelet transform. Cohen, Fan, and Attai (1991), Gupta and Sortrakul (1998) and Pyun et al. (2007) used a model-based method such as Gaussian Markov random field to inspect fabric defects and the method is computationally intensive. Fourier-based methods characterize the spatial-frequency distribution of images, but they do not consider the information in the spatial domain and may ignore local deviations (Chan & Pang, 2000; Tsai & Huang, 2003; Zhang & Bresee, 1995). The Artificial Neural Network (ANN) was developed to assess set marks but the parameter selection was inadequate and the results were unsatisfactory (Vangheluwe, Sette, & Pynckels, 1993). In 1996, Tsai and Hu (1996) classified the inputs of nine parameters obtained from a fabric image’s Fourier spectrum using the BP neural network. Nevertheless, the identification rate was not satisfactory. Gabor filters have been recognized as a joint spatial/spatial-frequency representation for analyzing textured images and detecting defects that contain highly specific frequency and orientation characteristics (Bovik & Clark, 1990; Coggins & Jain, 1989; Jain & Farrokhnia, 1991). A potential disadvantage of the decomposition of the Garbor filters is that they are computationally intensive. The Gabor filter banks are not mutually orthogonal, which may result in a significant correlation among texture features obtained from the Gabor-filtered images. Wavelet

Decision Support Systems 46 (2008) 411–419

Contents lists available at ScienceDirect

Decision Support Systems j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / d s s

Sales forecasting using extreme learning machine with applications in fashion retailing Zhan-Li Sun, Tsan-Ming Choi ⁎, Kin-Fan Au, Yong Yu Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

a r t i c l e

i n f o

Article history: Received 10 August 2007 Received in revised form 22 July 2008 Accepted 31 July 2008 Available online 13 August 2008 Keywords: Fashion sales forecasting Extreme learning machine Artiﬁcial neural network Backpropagation neural networks Decision support system

a b s t r a c t Sales forecasting is a challenging problem owing to the volatility of demand which depends on many factors. This is especially prominent in fashion retailing where a versatile sales forecasting system is crucial. This study applies a novel neural network technique called extreme learning machine (ELM) to investigate the relationship between sales amount and some signiﬁcant factors which affect demand (such as design factors). Performances of our models are evaluated by using real data from a fashion retailer in Hong Kong. The experimental results demonstrate that our proposed methods outperform several sales forecasting methods which are based on backpropagation neural networks. © 2008 Elsevier B.V. All rights reserved.

1. Introduction Sales forecasting refers to the prediction of future sales based on past historical data. Owing to competition [41,42] and globalization, sales forecasting plays a more and more prominent role in a decision support system [26] of a commercial enterprise. An effective sales forecasting can help the decision-maker calculate the production and material costs and determine the sales price. This will result in lower inventory levels, quick response and achieve the objective of just-in-time (JIT) delivery [2,5–7,12]. However, sales forecasting is usually a highly complex problem due to the inﬂuence of internal and external environments, especially for the fashion and textiles industry [25–27]. Thus, nowadays, how to develop more accurate and timely sales forecasting methods becomes an important research topic. Some retailers improve their stocking decisions by acquiring market information and revising their forecast in multiple stages [8–10]. A good forecasting method can help retailers reduce over-stocking and under-stocking costs [12]. Thus sales forecasting becomes one crucial task in supply chain management under uncertainty and it greatly affects the retailers and other channel members in various ways [31,43]. In this paper, we propose a new method which employs extreme learning machine (ELM) for sales forecasting in fashion retailing [32]. Recently, artiﬁcial neural networks (ANNs) have been applied extensively for sales forecasting [4,13,34,35,44–46] because they have very promising performance in the areas of control, prediction, and pattern recognition [15,21,22,30,33,38,40]. Many studies conclude that ANN is better than various conventional methods [1,3,28,29,39]. In

⁎ Corresponding author. Tel.: +86 852 27666450; fax: +86 852 27731432. E-mail address: tcjason@inet.polyu.edu.hk (T.-M. Choi). 0167-9236/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.dss.2008.07.009

[13], the statistical time-series model and the ANN based model were investigated for forecasting women's apparel sales. Chakraborty et al. [3] presented an ANN approach based on multivariate time-series analysis, which can accurately predict the ﬂour prices in three cities in USA. Lachtermacher and Fuller [28] developed a calibrated ANN model. In the model, the Box–Jenkins methods are used to determine the lag components of the input data. Moreover, it employed a heuristics method to choose the number of hidden units. In Kuo and Xue [27], the authors reported that the ANNs are better than many conventional statistical forecasting methods (see [3,16] for more details). However, most ANN based sales forecasting methods use gradient-based learning algorithms, such as the backpropagation neural network (BPNN), and problems such as over-tuning and long computation time still arise. A relatively novel learning algorithm for single-hidden-layer feedforward neural networks (SLFN) called extreme learning machine (ELM) has been proposed in [20,47] recently. In ELM, the input weights (linking the input layer to the hidden layer) and hidden biases are randomly chosen, and the output weights (linking the hidden layer to the output layer) are analytically determined by using the Moore– Penrose (MP) generalized inverse. ELM not only learns much faster with a higher generalization performance than the traditional gradient-based learning algorithms but it also avoids many difﬁculties faced by gradient-based learning methods such as stopping criteria, learning rate, learning epochs, local minima, and the over-tuned problems [16–18,36]. To the best of our knowledge, the application of ELM for fashion sales forecasting has not been studied in the literature. In this paper, the ELM is selected to analyze fashion sales forecasting on the data provided by a Hong Kong fashion retailer. In this method, some design factors (size, color, etc.) and sales factors (price, etc.) of the fashion apparels are chosen as the input variables of the ELM. Although ELM has many

RECOT: an expert system for the reduction of environmental cost in the texti... Kostas Metaxiotis Information Management & Computer Security; 2004; 12, 2/3; ABI/INFORM Global pg. 218

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

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IJCST 19,1

Prediction of the air permeability of woven fabrics using neural networks

18 Received March 2006 Revised August 2006 Accepted August 2006

Ahmet C¸ay Ege University, Textile Engineering Department, Izmir, Turkey

Savvas Vassiliadis and Maria Rangoussi Technological Education Institute of Piraeus, Department of Electronics, Athens, Greece, and

Is¸ık Tarakc¸ıog˘lu Ege University, Textile Engineering Department, Izmir, Turkey Abstract Purpose – The target of the current work is the creation of a model for the prediction of the air permeability of the woven fabrics and the water content of the fabrics after the vacuum drying. Design/methodology/approach – There have been produced 30 different woven fabrics under certain weft and warp densities. The values of the air permeability and water content after the vacuum drying have been measured using standard laboratory techniques. The structural parameters of the fabrics and the measured values have been correlated using techniques like multiple linear regression and Artificial Neural Networks (ANN). The ANN and especially the generalized regression ANN permit the prediction of the air permeability of the fabrics and consequently of the water content after vacuum drying. The performance of the related models has been evaluated by comparing the predicted values with the respective experimental ones. Findings – The predicted values from the nonlinear models approach satisfactorily the experimental results. Although air permeability of the textile fabrics is a complex phenomenon, the nonlinear modeling becomes a useful tool for its prediction based on the structural data of the woven fabrics. Originality/value – The air permeability and water content modeling support the prediction of the related physical properties of the fabric based on the design parameters only. The vacuum drying performance estimation supports the optimization of the industrial drying procedure. Keywords Air, Permeability, Neural nets, Porosity, Drying, Modelling Paper type Research paper

Introduction Textile fabrics consist of interlaced yarns or fibers. They are complex materials and their structure is porous. They permit the flow of the air through the constituting materials: yarns and fibers. The discussion about the air permeability of the textile International Journal of Clothing Science and Technology Vol. 19 No. 1, 2007 pp. 18-35 q Emerald Group Publishing Limited 0955-6222 DOI 10.1108/09556220710717026

The authors would like to acknowledge Clothing Textile and Fibre Technological Development SA, in Athens, Greece for the use of the Shirley air permeability tester and Go¨khan Tekstil San. Tic. A.S¸., in Denizli, Turkey, for the kind supply of the woven fabrics. The present work has been partially supported by the “Archimedes 1” EPEAEK Research Project in TEI Piraeus, co-financed by the EU (ESF) and the Greek Ministry of Education.

ARTICLE IN PRESS Engineering Applications of Artiﬁcial Intelligence 22 (2009) 272–282

Contents lists available at ScienceDirect

Engineering Applications of Artiﬁcial Intelligence journal homepage: www.elsevier.com/locate/engappai

Optimisation of garment design using fuzzy logic and sensory evaluation techniques Y. Chen a, X. Zeng a, , M. Happiette a, P. Bruniaux a, R. Ng b, W. Yu b a b

Ecole Nationale Supe´rieure des Arts & Industries Textiles, Roubaix 59100, France Institute of Textiles & Clothing, the Hong Kong Polytechnic University, Hong Kong, China

a r t i c l e in f o

a b s t r a c t

Article history: Received 22 May 2007 Received in revised form 22 April 2008 Accepted 30 May 2008 Available online 9 August 2008

The ease allowance is an important criterion in garment design. It is often taken into account in the process of construction of garment patterns. However, the existing pattern generation methods cannot provide a suitable estimation of ease allowance, which is strongly related to wearer’s body shapes and movements and used fabrics. They can only produce 2D patterns for fixed standard values of ease allowance. In this paper, we present a new method for optimizing the estimation of ease allowance of a garment using fuzzy logic and sensory evaluation. Based on the optimized values of ease allowance generated from fuzzy models related to different key body positions and different wearer’s movements, we obtain an aggregated ease allowance using the OWA operator. This aggregated result can further improve the wearer’s fitting perception of a garment and adjust the compromise between the style of garments and the fitting comfort sensation of wearers. The related weights of the OWA operator are determined according to designer’s linguistic criteria on comfort and garment style. The effectiveness of our method has been validated in the design of trousers of jean type. It can be also applied for designing other types of garment. & 2008 Elsevier Ltd. All rights reserved.

Keywords: Garment design Ease allowance Fuzzy logic Sensory evaluation Data aggregation OWA operator

1. Introduction Recently, mass customization has made great benefits in many manufacturing sectors, including automobile, textile, cosmetic and so on. Classically, this concept is defined as ‘‘producing goods and services to meet individual customer’s needs with near mass production efficiency’’ Tseng and Jiao (2001). Mass customizers can customize products quickly for individual customers or for niche markets at better than mass production efficiency and speed. Using the same principles, mass customizers can build-toorder both customized products and standard products without forecasts, inventory, or purchasing delays. In general, mass customization is realized by the use of flexible computer-aided manufacturing systems to produce custom output. In textile and garment industry, enterprises also pay great attention to mass customization and wish to quickly produce a great quantity of personalized garments meeting dynamically changing needs of consumers on garment comfort and style with low production and design cost. The garment design-computer aided system presented in this paper has been developed in this economic

Corresponding author at: The ENSAIT Textile Institute, GEMTEX Laboratory, 9 rue de l’Ermitage, 59100 Roubaix, France. Tel.: +33 320258967; fax: +33 320272597. E-mail address: xianyi.zeng@ensait.fr (X. Zeng).

background. The realization of this system is a multidisciplinary project which needs joint efforts of computer scientists, textile researchers, garment and fashion designers. It is not positioned in a unique scientific community but cover competences of several scientific and technical fields. A garment is assembled from different cut fabric elements fitting human bodies. Each of these cut fabric elements is reproduced according to a pattern made on paper or card, which constitutes a rigid 2D geometric surface. For example, a classical trouser is composed of cut fabrics corresponding to four patterns: front left pattern, behind left pattern, front right pattern and behind right pattern. A pattern contains some reference lines characterized by dominant points which can be modified. Of all the classical methods of garment design, the draping method is used in the garment design of high level Crawford (1996). Using this method, pattern makers drape the fabric directly on the mannequin, fold and pin the fabric onto the mannequin, and trace out the fabric patterns. This method leads to the direct creation of clothing with high accuracy but needs a very long trying time and sophisticated techniques related to personalized experience of operators. Therefore, it cannot be applied in a massive garment production. Direct drafting method is faster and more systematic but often less precise Aldrich (1997). It is generally applied in classical garment industry. Using this method, pattern makers directly draw patterns on paper using a pattern construction procedure, implement in a garment

European Journal of Operational Research 177 (2007) 1876–1893 www.elsevier.com/locate/ejor

Optimisation of fault-tolerant fabric-cutting schedules using genetic algorithms and fuzzy set theory P.Y. Mok a, C.K. Kwong a

a,*

, W.K. Wong

Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong b Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong Available online 25 January 2006

Abstract In apparel industry, manufacturers developed standard allowed minutes (SAMs) databases on various manufacturing operations in order to facilitate better scheduling, while effective production schedules ensure smoothness of downstream operations. As apparel manufacturing environment is fuzzy and dynamic, rigid production schedules based on SAMs become futile in the presence of any uncertainty. In this paper, a fuzzification scheme is proposed to fuzzify the static standard time so as to incorporate some uncertainties, in terms of both job-specific and human related factors, into the fabric-cutting scheduling problem. A genetic optimisation procedure is also proposed to search for fault-tolerant schedules using genetic algorithms, such that makespan and scheduling uncertainties are minimised. Two sets of real production data were collected to validate the proposed method. Experimental results indicate that the genetically optimised fault-tolerant schedules not only improve the operation performance but also minimise the scheduling risks. Ó 2005 Elsevier B.V. All rights reserved. Keywords: Genetic algorithms; Fuzzy set theory; Parallel machine scheduling; Fabric cutting

1. Introduction In response to the ever changing fashion markets, quick response to customer demands is a

* Corresponding author. Tel.: +852 2766 6610; fax: +852 2362 5267. E-mail address: mfckkong@inet.polyu.edu.hk (C.K. Kwong).

key philosophy of today’s apparel industry. In order to shorten products’ time to market, planning and scheduling of various apparel manufacturing operations have received large research attention recently. Algorithms and heuristics developed in cutting and packing problems (Bennell et al., 2001; Burke et al., 2004, in press; Dowsland et al., 2002; Gomes and Oliveira, 2006; Jakobs, 1996; Kim et al., 2001; Hiﬁ and M’Hallah, 2005) are being applied in marker planning for fabric

Expert Systems with Applications 36 (2009) 8571–8581

Contents lists available at ScienceDirect

Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa

Optimal reorder decision-making in the agent-based apparel supply chain A. Pan a, S.Y.S. Leung a,*, K.L. Moon a, K.W. Yeung b a b

Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong Clothing Industry Training Authority, Hong Kong

a r t i c l e Keywords: Agent Decision-making Reorder strategy

i n f o

a b s t r a c t The application of agent technology in the apparel supply chain management has gained increasing interest. Agents can help automate a variety of tasks and facilitate decision-making in the supply chain. Compared with other industries, there are more uncertainties existing in the fashion industry such as market needs, fashion change and seasonality, which increases the difﬁculty of managing the apparel supply chain especially in the ordering process. Thus, it is necessary to increase the coordination in the apparel supply chain processes and develop optimal decision-making strategy for the apparel supply chain under the dynamic environment. In this paper, uniﬁed modeling language (UML) is applied to simulate the supply chain processes and describe the relationships between agents. This paper also applies genetic algorithm (GA) and fuzzy inference theory to the dynamic reorder strategy for the supply chain agent to make optimal decision about replenishment quantity and reorder point in order to minimize the inventory cost correspondingly. Ó 2008 Elsevier Ltd. All rights reserved.

1. Introduction In today’s increasingly global and competitive clothing marketplace, it is imperative that apparel enterprises work together to achieve common goals such as minimizing the delay of deliveries, the holding and the transportation costs (Roy, Anciaux, Monteiro, & Ouzizi, 2004). A supply chain can be defined as a network consisting of suppliers, warehouses, manufacturers, wholesales, and retailers through which material and products are acquired, transformed, and delivered to consumers in markets (Hyung & Sung, 2003). Thus, more and more apparel companies adopt and explore better supply chain management (SCM) to improve the overall efficiency. A successful SCM requires a change from managing individual functions to integrating activities into key supply chain processes. Owing to the high complexity and uncertainty of the supply chain in apparel industry, a traditional centralized decisional system seems unable to manage easily all the information flows and actions. Thus, a more distributed approach, agent technology is reviewed in this research in order to achieve better operation and to facilitate the apparel supply chain management. Generally speaking, agents are active, persistent (software) components with the abilities of perceiving, reasoning, acting and communicating (Fung & Chen, 2005). The agent may follow a set of rules predefined by the user and then applies them. The intelligent agent will learn and be able to adapt to the environment in terms of user requests consistent with the available resources (Papazoglou, 2001). The key aspects of agents are their autonomy * Corresponding author. Tel.: +852 27666467. E-mail address: sunney.leung@polyu.edu.hk (S.Y.S. Leung). 0957-4174/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2008.10.081

and abilities to reason and act in their environment, as well as to interact and communicate with other agents to solve complex problems (Jain, Aparico, & Singh, 1999). Autonomy means that the agent can act without the direct intervention of humans or other agents and that it has control over its own actions and internal state. The agent must communicate with the user or other agents to receive instructions and provide results. An essential quality of an agent is the amount of learned behavior and possible reasoning capacity that it has. As the market needs are extremely various and fashion updates quickly, the supply chain member usually cannot make decisions immediately because of the inaccurate or incomplete information. The decision delay in the supply chain prolongs the process time and causes a company to lose competence. In order to reduce this delay, the supply chain member needs to give quick response. Thus, a supply chain can be characterized as a logistic network of partially autonomous decision-makers. Supply chain management has to do with the coordination of decisions within the network. Different segments of the network are communicating with one another through ﬂows of material and information, being controlled and coordinated by the activities of supply chain management. Since more than one decision-maker is involved, the supply chain has a typical distributed decision-making situation (Schneeweiss, 2003). In the apparel supply chain, ordering decision and inventory decision are two critical decisions supply chain managers have to face. The orders are usually made based on the forecasted customer demand without considering the uncertain factors in apparel industry such as weather and fashion trend. Researches have been done on the vendor-managed inventory (VMI) replenishment

DyesandPigments,Vol. 34, No. 3, pp. 181-193, 1997

ÂŠ 1997ElsevierScienceLtd Great Britain 0143-7208/97517.00+0.00

PII:

S0143-7208(96)00081-2

ELSEVIER

Multiple Linear Regression ( M L R ) and Neural Network ( N N ) Calculations o f some Disazo Dye Adsorption on Cellulose" Simona Timofei, a* Ludovic Kurunczi, a Takahiro Suzuki, b Walter M. F. Fabian c & Sorel Mure~and alnstitute of Chemistry, Romanian Academy, Bul. Mihai Viteazu124, 1900, Timisoara, Romania bResearch Laboratory of Resources Utilization, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226 Japan qnstitut fiir Organische Chemie, Kari-Franzens-Universit~it Graz, Heinrichstrasse 28, A-8010, Graz, Austria apolitehnica University of Timisoara, Faculty of Industrial Chemistry, Pial;a Victoriei Nr. 2, 1900, Timisoara, Romania (Received 15 July 1996; accepted 16 August 1996)

A BSTRA CT Multiple Linear Regression ( M L R ) analysis and Neural Network ( NN) calculations are applied to a series of 21 disazo anionic dyes. Three-dimensional QSAR parameters were derived from the Cartesian coordinates of the dye molecules. Low energy conformations were obtained by molecular mechanics and quantum chemical calculations. Electronic and steric effects in the dyecellulose binding are present. The proposed M L R models are rough approximations of nonlinear models. Good correlation with the dye affinity from the M L R calculations and a significantly improved fitting of the N N over the M L R models are observed. The model validity was checked for two proposed models derived from differet:t sets of structural parameters by the leave-oneout cross-validation procedure. For the first model, a better validity ('crossvalidated r2' value, of 0.622) of the N N model is noticed by leaving out one compound (found as outlier) from the training set, in comparison to that of the M L R model obtained for the same set of compounds (q2= 0.434). The q2 value of a second M L R proposed model is better than that of the corresponding NN model. ÂŠ 1997 Elsevier Science Ltd Keywords: Multiple Linear Regression (MLR) analysis, Neural Networks (NNs), dye adsorption, cellulose fibre. ~Presented in part at the Symposium on Computational Chemistry, held on 16-17 May 1996 in Tokyo, Japan *Corresponding author. 181

Chemical Engineering and Processing 42 (2003) 645 /652 www.elsevier.com/locate/cep

Modelling of the flow behavior of activated carbon cloths using a neural network approach Catherine Faur-Brasquet *, Pierre Le Cloirec Ecole des Mines de Nantes, DSEE-GEPEA, UMR CNRS 6144, 4 rue A. Kastler, BP 20722, 44307 Nantes Cedex 3, France Received 12 April 2002; received in revised form 30 September 2002; accepted 30 September 2002

Abstract This work investigates the hydrodynamic and aerodynamic behaviors of recent adsorbents, activated carbon cloths (ACC). A first part presents their characteristics, a particular attention being given to the properties related to their woven structure. The influence of these characteristics on air and water pressure drops through ACC is shown by experimental measurements. It is established that a classic model set up for particular media, the Ergun model, does not enable a satisfying modelling of experimental data. An artificial neural network (ANN) is then used in order to include as explicative factors the cloths properties. The optimization of the ANN architecture is carried out, in terms of selection of the input neurons and number of hidden neurons. The generalization ability of the ANN is evaluated using a test dataset distinct from the training set. The influence of specific characteristics of cloths on their flow behavior is confirmed by an analysis of inputs sensibility, and the determination of their predictive influence. # 2003 Elsevier Science B.V. All rights reserved. Keywords: Neural network; Flow behavior; Activated carbon cloths; Ergunâ&#x20AC;&#x2122;s model

1. Introduction Activated carbon (AC) in the form of granules (GAC) or powder (PAC) is commonly used for air or water treatment [1,2] and granular activated carbon has been proved to be effective in removing a large number of organic molecules [3]. Recently, a new form of AC has appeared: activated carbon fibers (ACF) which may be pressed to form a felt or woven as a cloth. This last material, activated carbon cloth (ACC), has been extensively studied in terms of adsorption performance in aqueous and gaseous phase. Some researches have shown their interesting adsorption properties for microorganics [4], metal ions [5], dyes [6] in wastewaters, and for volatil organic compounds contained in polluted gaseous streams [7]. Their high specific surface area (up to 1900 m2 g 1) coupled with the low diameter of fibers (around 10 mm) and some micropores directly connected to the external surface area of fibers, enable adsorption

* Corresponding author. Tel.: /33-251-85-8294; fax: /33-251-858299. E-mail address: catherine.faur@emn.fr (C. Faur-Brasquet).

rates 1.2 /20 times greater that those obtained with a commercial granular activated carbon. High adsorption capacities are also found, reaching up 400 mg g 1 [8]. Furthermore, the woven form allows new kinds of reactors to be imagined and designed. However, this industrial design requires the study of the flow behavior of ACC, in terms of hydro and aerodynamic properties. Literature reports few information related to pressure drops through woven structures. Generally, the works on the dynamic behavior of fibrous media deal with random stacking of glass fibers [9,10], wood fibers [11] or textile fibers [12,13], i.e. some structures which have little in common with woven media from a geometric point of view. These studies use some models close to the Ergun equation [14] whose parameters were empirically calculated for the fibrous media. Other works were carried out with fabrics and set a relationship between the permeability of fabrics to air and their degree of opening but only in the case of a laminar flow [15,16]. The lack of specific deterministic models, coupled with the multiparameter dimension of fabrics aerodynamics led us to consider a neural network approach to model fluid pressure drops through ACC. Artificial neural networks (ANN) are algorithmic systems derived

Dyes and Pigments 75 (2007) 356e361 www.elsevier.com/locate/dyepig

Modelling of CIELAB values in vinyl sulphone dye application using feed-forward neural networks M. Senthilkumar* Department of Textile Technology, PSG College of Technology, Coimbatore 641 004, Tamilnadu, India Received 20 June 2005; received in revised form 18 August 2005; accepted 7 June 2006 Available online 10 August 2006

Abstract Artificial neural network (ANN) technology has developed from the experimental stage into real industrial applications. To achieve this significant transition, careful planning and adjustment are required. This article is concerned with the CIELAB values’ prediction based on a neural network developed for cotton fabric dyed with vinyl sulphone reactive dye. The neural network developed is a multilayer feed-forward network. In textile dyeing industry, achieving the required depth of colour is the important task. In this paper, to achieve the required depth of colour, the CIELAB values of the fabric to be dyed were predicted using trained feed-forward neural network. The results obtained from the network gives an average error of around 2.0% for vinyl sulphone dyes used for training the network in predicting the LAB values. The trained network brings out the same error for other dyes as well as for input and output parameters selected beyond the range used for training the network. Ó 2006 Elsevier Ltd. All rights reserved. Keywords: Colour measurement; Neural networks; Reflectance; Total dye fixed; Whiteness index

1. Introduction Today, the highly competitive marketplace requires a strong commitment of firms to satisfy customer’s expectations. This tendency is even more pronounced for the product appearance. The textile field is especially sensitive to this phenomenon. One of the most important textile characteristics is undoubtedly colour. Among the many quality parameters to be achieved in the dyed goods, achieving the appropriate depth of shade is a very important one. If the depth of colour produced is different from what is expected, the material has to be either taken for reworking or rejection. So to proceed further colour of the dyed goods has to be measured. For the measurement of colour, standard values are used worldwide, for example as determined by an organisation called CIE. The values used by CIE 1976 are called L*, a*

* Tel.: þ91 422 2572177x4169, þ91 9443948513 (Mob); fax: þ91 422 2573833. E-mail address: msksenthilkumar@yahoo.com 0143-7208/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.dyepig.2006.06.010

and b* and the colour measurement method is called CIELAB. L* represents the difference between light (where L* ¼ 100) and dark (where L* ¼ 0), a* represents the difference between green ( a*) and red (þa*) and b* represents the difference between yellow (þb*) and blue ( b*). The CIE 1976 L*, a* and b* colour space or CIELAB colour space is defined by quantities L*, a* and b*. This L*, a* and b* values are calculated after dyeing the material and based on this values the material will be either taken for next processing or reworking. The L* a* b* values for a given situation can be predicted using statistical tools such as multiple regression analysis or computational processors such as artificial neural networks (ANN). Prediction using ANNs is claimed to have better accuracy compared to multiple regression analysis [1,2]. In recent days, neural networks are used for modelling nonlinear problems and to predict the output values for a given input parameters from their training values. Most of the textile processes and quality assessments are non-linear in nature and hence neural networks find application in textile technology. Web density control in carding [3], prediction of yarn strength [4], ring and rotor yarn hairiness [5], total hand

Expert Systems with Applications 36 (2009) 4268–4277

Contents lists available at ScienceDirect

Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa

Intelligent production control decision support system for ﬂexible assembly lines Z.X. Guo, W.K. Wong *, S.Y.S. Leung, J.T. Fan Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong, China

a r t i c l e

i n f o

Keywords: Production control Decision support system Flexible assembly lines Genetic algorithms Radio frequency identiﬁcation Learning curves

a b s t r a c t In this study, a production control problem on a flexible assembly line (FAL) with flexible operation assignment and variable operative efficiencies is investigated. A mathematical model of the production control problem is formulated with the consideration of the time-constant learning curve to deal with the change of operative efficiency in real-life production. An intelligent production control decision support (PCDS) system is developed, which is composed of a radio frequency identification technology-based data capture system, a PCDS model comprising a bi-level genetic optimization process and a heuristic operation routing rule is developed. Experimental results demonstrated that the proposed PCDS system could implement effective production control decision-making for solving the FAL. Ó 2008 Published by Elsevier Ltd.

1. Introduction Effective production control is useful and necessary to improve production and management performances and reduce the running cost of factories. A generic architecture for production control decision-making is shown in Fig. 1. In a real-life production environment, production data on production orders, production quantities of each workstation and the whole production line, operative efficiency, etc., are collected from shop floors or assembly lines by using various types of data capture methods, including the manual recording method, barcode scanning, and the most updated radio frequency identification (RFID) technology. Based on the collected production data, the production manager makes production decisions to achieve various production objectives. On shop floors or assembly lines with a low level of automation, it is impossible to obtain real-time production data owing to the absence of an effective data capture system. Thus, it is also impossible to make accurate and real-time decisions for production control. This paper presents an intelligent production control decision support (PCDS) system, which is integrated with an RFID-based real-time data capture system, for assisting in the production control decisions on a flexible assembly line (FAL).

review on manufacturing flexibility. There are various types of manufacturing flexibility such as machine flexibility and routing flexibility. Machine flexibility is measured by the number of operations that a workstation processes and the time needed to switch from one operation to another. The more operations a workstation processes, the less time switching takes and the higher the machine flexibility becomes. Routing flexibility is the ability of a production system to manufacture a product using several alternative routes in the system and it is usually determined by the number of such potential routes. The FAL is an increasingly attractive assembly form for small or mid-scale production in many industries. Unlike the traditional assembly line, some FALs allow flexible operation assignment, where one operation can be assigned to multiple workstations for processing and multiple operations can be assigned to the same workstation. When one operation is assigned to multiple workstations, the processing of this operation is shared by the assigned workstations and this operation is taken as a shared operation. Each shared operation of a product should be routed to an appropriate workstation on a real-time basis. Obviously, the FAL with flexible operation assignment involves machine flexibility and routing flexibility. In practice, this type of FAL is usually used in apparel manufacturing.

1.1. Manufacturing flexibility and flexible assembly lines 1.2. Variability of operative efficiency To meet the increasingly fierce market competition, more and more manufacturing enterprises seek benefits from manufacturing flexibility and effective production control. Beach, Muhlemann, Price, Paterson, and Sharp (2000) provided a comprehensive * Corresponding author. Tel.: +852 27666471; fax: +852 27731432. E-mail address: tcwongca@inet.polyu.edu.hk (W.K. Wong). 0957-4174/$ - see front matter Ó 2008 Published by Elsevier Ltd. doi:10.1016/j.eswa.2008.03.023

On a highly automated assembly line, the efficiency to process a certain task is deterministic. Yet on FALs with a low level of automation, e.g., FALs highly relying on manual efforts, the operative efficiency of each task is seldom constant. The variable operative efficiency leads to the fluctuation of the actual cycle time and increases the complexity of production control.

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Expert Systems with Applications Expert Systems with Applications 35 (2008) 1788–1801 www.elsevier.com/locate/eswa

Genetic optimization of order scheduling with multiple uncertainties Z.X. Guo, W.K. Wong *, S.Y.S. Leung, J.T. Fan, S.F. Chan Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong, China

Abstract In this paper, the order scheduling problem at the factory level, aiming at scheduling the production processes of each production order to different assembly lines is investigated. Various uncertainties, including uncertain processing time, uncertain orders and uncertain arrival times, are considered and described as random variables. A mathematical model for this order scheduling problem is presented with the objectives of maximizing the total satisfaction level of all orders and minimizing their total throughput time. Uncertain completion time and beginning time of production process are derived firstly by using probability theory. A genetic algorithm, in which the representation with variable length of sub-chromosome is presented, is developed to generate the optimal order scheduling solution. Experiments are conducted to validate the proposed algorithm by using real-world production data. The experimental results show the effectiveness of the proposed algorithm. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Order scheduling; Uncertain processing time; Probability theory; Genetic algorithms

1. Introduction Facing with ever increasing global market competition, today’s manufacturers have to continuously improve their production performance so as to be more competitive in the market. Effective production scheduling plays a significant role in maximizing the resource utilization and shortening the production lead time. A wide literature base has been published on production scheduling, focussing mostly on the scheduling for various types of production systems at the shop floor or assemblyline level, such as job shop scheduling (Adam et al., 1993; Fayad and Petrovic, 2005; Guo et al., 2006; Kondakci and Gupta, 1991), flow shop scheduling (Ishibuchi, Yama moto, Murata, & Tanaka, 1994; Iyer & Saxena, 2004; Morita & Shio, 2005; Nagar, Heragu, & Haddock, 1996), machine scheduling (Baek & Yoon, 2002; Dimopoulos & Zalzala, 2001; Fowler, Horng, & Cochran, 2003; Liu & Tang, 1999), assembly line scheduling (Guo et al., 2006; Kaufman, 1974; Vargas et al., 1992; Zhang et al., 2000), *

Corresponding author. Tel.: +852 27666471; fax: +852 27731432. E-mail address: tcwongca@inet.polyu.edu.hk (W.K. Wong).

etc. Ashby and Uzsoy (1995) have presented a set of scheduling heuristic to solve the order release and order sequencing problem in a single-stage production system. Axsater (2005) has discussed the order release problem in a multistage assembly network by an approximate decomposition technique. Their studies only focused on determining the starting times for different processes of each production order (also called order), where the process should be performed has not been considered. Chen and Pundoor (2006) have considered the order assignment and scheduling in the supply chain level, they focused on assigning orders to different factories and finding a schedule for processing the assigned orders at each factory. However, multiple shop floors and multiple assembly lines are setup in most factories. The order scheduling problem at the factory level, where the production process of each order scheduled to the appropriate assembly line, has not been reported so far. The great majority of previous studies on production scheduling are based on the deterministic estimation of the processing time of each production process and the arrival time of each order. In real-life production environment, various uncertainties often occur, such as uncertain customer orders and uncertain estimation of processing

J Intell Manuf (2006) 17:341–354 DOI 10.1007/s10845-005-0007-8

Genetic optimization of JIT operation schedules for fabric-cutting process in apparel manufacture W.K. Wong · C.K. Kwong · P.Y. Mok · W.H. Ip

Abstract Fashion products require a significant amount of customization due to differences in body measurements, diverse preferences on style and replacement cycle. It is necessary for today’s apparel industry to be responsive to the ever-changing fashion market. Just-in-time production is a must-go direction for apparel manufacturing. Apparel industry tends to generate more production orders, which are split into smaller jobs in order to provide customers with timely and customized fashion products. It makes the difficult task of production planning even more challenging if the due times of production orders are fuzzy and resource competing. In this paper, genetic algorithms and fuzzy set theory are used to generate just-in-time fabric-cutting schedules in a dynamic and fuzzy cutting environment. Two sets of real production data were collected to validate the proposed genetic optimization method. Experimental results demonstrate that the genetically optimized schedules improve the internal satisfaction of downstream production departments and reduce the production cost simultaneously. Keywords Genetic algorithms · Fuzzy set theory · Parallel machine scheduling · Fabric cutting · Apparel

W.K. Wong (B) · P.Y. Mok Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong e-mail: tcwongca@inet.polyu.edu.hk C.K. Kwong · W.H. Ip Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong

Introduction Apparel production is a type of assembly manufacture that involves a number of processes. Fabric-cutting operation is done in a fabric-cutting department, which usually serves several downstream sewing assembly lines. Effective upstream fabric-cutting operation ensures the smoothness of downstream operations, and thus is vitally important to the overall system efficiency. Production scheduling of apparel production is a challenging task due to a number of factors. First of all, fashion trend is always unpredictable, thus just-in-time production is employed to ensure products’ short time-to-market. Moreover, in order to cope with the increasing demand on product customization, the quantity of garments per production order tends to be smaller and thus number of production order processed by the manufacturer has been becoming larger. In this paper, just-in-time (JIT) production scheduling of manual cutting department operation is investigated. JIT scheduling Production scheduling has been extensively studied, and the previous literature has more focused on some single regular measures, such as mean flow-time and mean lateness. Since the 1980s, the concept of penalizing both earliness and tardiness has spawned a new and rapidly developing line of research in the scheduling field (Baker & Scudder, 1990). In a JIT environment, both earliness and tardiness must be discouraged since early finished jobs increase inventory cost while late jobs lead to customers’ dissatisfaction and loss of business goodwill. Thus an ideal schedule is one in which all jobs finish within the assigned due dates. The objectives of early/tardy (E/T) scheduling could be interpreted in different ways, for example minimizing total absolute deviation from due dates, job dependent earliness and tardiness penalties,