Vehicle scheduling problem research proposal tutorsindia by Tutors India

Research Proposal Vehicle scheduling problem preferably its application in Electric /alternatives fuel city bus fleets

ÂŠ 2017-2018 All Rights Reserved, No part of this document should be modified/used without prior consent Tutors Indiaâ&#x201E;˘ Your trusted mentor since 2001 I www.tutorindia.com UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX I UK # +44-1143520021, Info@tutorsindia.com

Table of Contents 1.0 Introduction ............................................................................................................................................. 3 2.0 Background of the study ......................................................................................................................... 3 3.0 Previous Work ........................................................................................................................................ 5 4.0. Problem Description .............................................................................................................................. 8 5.0. Aim and Research Objectives ................................................................................................................ 9 6.0. Expected outcome .................................................................................................................................. 9 7.0 Research Plan ........................................................................................................................................ 10 References ................................................................................................................................................... 11

1.0 Introduction Various reports have been produced estimating the global damage cost of carbon emissions (Clarkson & Deyes, 2002; AEA Technology Environment, 2005; SEI, 1999; Stern, 2006). Therefore, it is very important to find the necessary ways for reducing the carbon-di-oxide in road freight transport. An operational issue facing the transport sector will be decisions relating to the routing and vehicular scheduling, and the choice of vehicle type for given deliveries, particularly in relation to the potential added cost of CO2 emissions. Thus, there is a need for better operational routing models that would identify the types of vehicles to purchase and how to schedule them, and eventually that would lead to maximize profit. Thus, the present study aims to solve a vehicle routing problem from the perspective of reduction of emission and fuel consumption.

2.0 Background of the study Vehicle scheduling problem has been extensively studied for over 50 years now (Dantzig & Ramser, 1959). Figure 1 explains about the relationship of a public transport company’s four operational planning problems found in the traditional planning process. Figure 1: Traditional Planning Process

Line frequencies

Time tabling Vehicle scheduling

Crew scheduling Collective agreements and labour rules

Crew rostering

© 2017-2018 All Rights Reserved, No part of this document should be modified/used without prior consent Tutors India™ Your trusted mentor since 2001 I www.tutorindia.com UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX I UK # +44-1143520021, Info@tutorsindia.com

Source: Adopted from Huisman, Freling & Wagelmans, 2004 Currently, models for Vehicle Scheduling Problems (VSP) are being used extensively for the needs of operational planning in public transportation. Bus transport has probably benefitted the most by the abundance of solution approaches created to optimize real-life problems of assigning busses to timetabled trips. In the conventional VSP, specifically, a set of timetabled trips with start/end locations, fixed travel times are given. The purpose of this method is to assign the vehicles to trips, in order to minimize the overall costs, each trip is covered once and each vehicle covers a feasible sequence of trips. Variations of the problem mainly stem from the focus on the two types of costs â&#x20AC;&#x201C; fixed (capital costs, maintenance) and operational (fuel and labour). Usually vehicle initial investments have a priority over the operational costs minimization. The paper of (Bunte & Kliewer, 2009) provides a concise survey of the VSP, its most popular cases and corresponding solution methodologies. With recent years of growing environmental concerns and government attempts on reducing vehicle emissions, a good opportunity for using the tools of Operations Research arises. Taking in consideration the fact that most developed countries have decided on placing quotas, the value of emissions or actually their reduction has been monetized. This in return creates a good incentive for trading and respectively minimization of toxic pollutants. Hence, in order to stay competitive in this new environment, it will be beneficial for urban transport companies to reconsider their scheduling capabilities and fleet so that costs and emissions are maintained at optimal ratio. This can be achieved by using a vehicle scheduling model that with a combination of different conventional, alternative fuel, hybrid or electric vehicles will cover a set of timetabled trips at the lowest possible cost and emissions. In short, the firm can optimize its decision on what types of vehicles to purchase and how to schedule them, in order to maximize profit. As an indirect result, the environment will also benefit from reduced pollution in cities. Little research has been done in that specific area of interest. To my knowledge, only one study (Jing-Quan Li, 2009) developed a bus scheduling model that aims at minimizing the environmental impact of a bus company. The present study ÂŠ 2017-2018 All Rights Reserved, No part of this document should be modified/used without prior consent Tutors Indiaâ&#x201E;˘ Your trusted mentor since 2001 I www.tutorindia.com UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX I UK # +44-1143520021, Info@tutorsindia.com

will attempt to develop a VSP with the extension of VTG (Vehicle Type Groups) that will use a time-space network to avoid an explosive increase of the model size due to the growing timetable of transport companies.

3.0 Previous Work Previous studies on sustainability from transport developed a vehicle replacement optimization focusing on preventive maintenance and replacement (Khalil, 2000), time dependent vehicle routing context using a tabu search procedure Jabali & Woensel, 2012), model that drivers chooses emission minimizing routes (Sugawara & Niemeier, 2002; Taniguchi et al., 2001), identification of shortest path (Scott et al., 2010) and problem to account pollution routing problem (Bektas & Laporte, 2011). Other studies on problems of vehicle scheduling focused on solutions to optimization problems. Studies by Rardin and Uzsoy (2001) and Lee and Jung (1989) developed heuristics optimizations while the recent study by Babu, Jerald, Haq and Luxmi (2010) addressed through meta-heuristic differential evolution (DE) algorithm. There is a vast amount of academic literature covering VSP some solutions or models approaches (Daduna & Paixao, 1995; Mesquita & Paixao, 1999; Mesquita & Paixao, 1999; Bodin & Golden, 1981; Bodin & Golden, 1983; Wren, 2003), and nearly all of them focus on trying to improve the type of heuristic used to produce an optimum result. For instance the study Sexton (1979) and Sexton and Bodin (1985a, 1985b) developed single vehicle DARP to solved a routing problem through an insertion heuristics (Cordeau & Laporte, (2003). Similarly, Yan (1988) proposed a heuristic method for truck scheduling while Ferland and Fortin (1989) investigated problem with sliding time windows using heuristic problem. This is the first method to address the time window problem. Moreover, several studies have also addressed the problem from the single-depot vehicle type (SDVSP). Daduna and Paixao (1995), Desrsiers et al., (1995) have listed several algorithms and applications to solve SDVSP such as linear assignment problem, a transportation problem, a minimum co (Freling et al., 2001) st flow problem, and a matching problem and a quasi experimental (Freling et al., 2001). In addition, vehicle scheduling problem was addressed ÂŠ 2017-2018 All Rights Reserved, No part of this document should be modified/used without prior consent Tutors Indiaâ&#x201E;˘ Your trusted mentor since 2001 I www.tutorindia.com UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX I UK # +44-1143520021, Info@tutorsindia.com

using network flow (Ahuja & Magnanti., 1993), Time constraint routing and scheduling (Desrsiers et al., 1995), Linear network optimization (Bertsekas, 1991), Dual coordinated step method to MDVSP (Eckstein, 1988), a colum generation approach (Ribeiro & Soumis, 1994), a forward or reverse action alogorithms (Castanon, 1993), Hungarian Assignment Algorithm (Volgenante, 1996), Systemtaic changes in the time table ( Hasselstrom, 1980), Quasi Assignment problem (Zhou, 1990), Bus scheduling with a fixed number of vehicles, time constraint and routing (Dumas et al., 1995) and integrated approach (Boender et al., 1995), Matching (Gerards, 1995), Type vehicle (Scott, 1986) Solomon et al., (1988) set the standard with six test problems applied to a heuristic that produced a set of routes for 100 customers with identical Euclidian times and distances. Subsequent academic research has tended to use these same Solomon test problems and to benchmark the results produced against Solomon’s results. A number of variants to the VRP heuristics have been developed to cope with operational requirements such as limitations on the capacity of a vehicle (CVRP), multi-depot Vehicle Routing and Scheduling Problem with Time Window (VRSPTW), with backhauls (VRPB), Open Vehicle Routing Problem with Pickup and Delivery (VRSPPSD), pickup and delivery the time window have been extensively studied. For instance, recently Liu (2013) addressed the problem with multi-distribution centre vehicle routing problem (Liu, 2013) with time window using artificial bee colony algorithm based genetic algorithm. The new algorithm not only lowered transportation costs and also increased economic efficiency. Most of the previous work focused on minimizing the number of vehicles, and some on total waiting time but not many on reduction of emission. The study by Apaydin and Gonullu (2008) developed a VRSP model for waste collection, particularly in this study author focused to optimize solid waste collection. However, Kuo focused on calculation of total fuel consumption for the TDVRP using simulated annealing (SA) algorithm (Kuo, 2010). Load and Speed was accounted by Bektas and Laporte to address the issue of emission (Bektas & Laporte, 2011). Further, Suzuki (2011), Wygonik and Goodchild (Erdoğan & Miller-Hooks, 2012) developed for pick-up and delivery system and Erdogan and Hook focused to minimize total distance travel © 2017-2018 All Rights Reserved, No part of this document should be modified/used without prior consent Tutors India™ Your trusted mentor since 2001 I www.tutorindia.com UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX I UK # +44-1143520021, Info@tutorsindia.com

(13). The main drawback with these classical techniques is that they often find suboptimal solutions by getting trapped in local minima. To overcome this problem, academic approaches have focussed on meta-heuristic techniques such as tabu search, simulated annealing and genetic algorithms, and some of these have started to appear in commercial packages (Slater, 2006). These metaheuristic techniques use high level algorithmic approaches to search for feasible solutions. Moreover, the traditional vehicle scheduling problem attempts to minimize capital and operating costs but failed to focus on pollutants which have become an increasingly important. In order to address this issue, recent study by Jing-Quan Li (2009) proposed an approach which is based on time space network for reducing the underlying network CPLEX’s, number of arcs to solve the problem. The findings of the study indicated that a significant reduction in the bus emission can be achieved if the bus-scheduling is proper in turn the carbon footprint. In addition several studies conducted in the field of VSP focused routing and scheduling problems in general and used several models or solution approaches to solve the problem. For instance, for single depot case models, proposed minimal decomposition model to solve SD-VSP. The idea was based on the Dilworth Theorem but main drawback is it only solved the smallest fleet size and no upper limit for the fleet size despite the operational costs. However, this drawback is fixed by the Assignment model where it fixes both an arrival and a departure node and used in multiple depot case. Again, this model could not predict a fixed or maximal number of vehicles. To overcome these limitations, the transportation model (Quasi Assignment model) was developed where only arcs with i (alpha) j are inserted in addition with two depot nodes. However, the limitation is it focuses only on short deadhead arcs. A network flow model (4) for tanker scheduling was proposed where a trip connected two notes which represented each trips. In multiple depot case for scheduling problems of vehicles from different locations, several models have been developed such as Single commodity, Multi-commodity and Set partitioning models. Since, the present study focused on multi-commodity formulation, will restrict our discussion only to ‘time s-pace networks’ which has a different underlying network structure. This avoids the disadvantage of explicit consideration of whatever possible connections involving compatible trips. © 2017-2018 All Rights Reserved, No part of this document should be modified/used without prior consent Tutors India™ Your trusted mentor since 2001 I www.tutorindia.com UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX I UK # +44-1143520021, Info@tutorsindia.com

Forbes et al. (1994) and Löbel (1998) consider such an extension within a connectionbased1 multi-commodity approach and the application within a time-space network was done in (Kliewer, Melloul, & Suhl, 2006). As this will make the problem NP-hard (Lenstra & Kan, 1981), a station-based time-space network can be used, which will reduce dramatically the number of arcs in the network and will allow a solution to optimality even for large instances using CPLEX, as applied by (Kliewer, Melloul, & Suhl, 2006). A key component in the formulation of the model will be the constraint on emissions, which can be relaxed in an elastic formulation whereby it is transformed to a penalty cost that involves the current market price of emissions. In this way, the company will be able to make the decision between allowing more emissions and buying quotas or cutting them and selling instead. The result should be an optimal fleet composed of different buses that are covering the schedule at lowest possible cost/emissions ratio to maximize profits.

Given the above literature, yet, to our knowledge, the explicit integration of vehicle type group using time-space network and algorithms has not been investigated in depth so far except the previous study. As previous studies have focused fixed, speed, load, road gradient and acceleration rate with different algorithms. In this study, the focus is to study the efficiency of vehicle routing through VGT and performance of this grouping will be addressed through total travel distance, total travel time, total fuel consumption, total waiting time and number of vehicle utilized per trip. The model used in this paper is based on a time-space network based modelling approach

4.0. Problem Description So far, there are no studies available about the vehicle scheduling problem to the best of our knowledge from sustainability point of view except the study by Jing-Quan Li (2009). Given, this gap and the gaps highlighted previously, the goal of this research is to develop a new mathematical model for the VRP with the extension of VTG, based on the Time space network.

The mathematical model proposed is tested in a set of XX instances which come from the public transportation system in XXX.

5.0. Aim and Research Objectives 1. To develop a new mathematical model for the VSP with the extension of Vehicle Type group (VTG) using a time-space network 2. To verify the effectiveness in terms of its performance and stability of the algorithm 3. To compare the adaptability of algorithms with previously developed algorithms

6.0. Expected outcome While various minimisation principles are used the emission value and route value gets change and this can be measured with the help of above findings. This study acts as a contribution from the academic end where the model is developed in the form of speed flow method by bridging two components, one from transportation planning in the form of driving cycles and another from vehicle emission component in the form of fuel consumption formulae by executing the VRP model, that is found in the field of logistics. Moreover in the additional academic contribution the way speed has been enhanced and it is constituted within the model of VRP. Government and operators can utilise practical contribution in current argument on CO2 emissions.

7.0 Research Plan

Things to do

Time Duration (Per week) 0 1

Draft

1 3

proposal

submission Final proposal Literature Review Ethical approval

for

committee conducting

research Collection Of Data Analysis of Data Submission of Report for comments 1st revision Submission of Report for comments 2nd revision Final Report Presentation of Thesis

References AEA Technology Environment (2005) The social cost of carbon (SCC) review methodological approaches for using SCC estimates in policy assessment, London: DEFRA Ahuja, R. K. and Magnanti, T. L. (1993) Network Flows: Theory, Algorithms and Applications, NJ, Prentice Hall. and crews: the state of the art’, Computers & Operations Research, 10(2), pp. 63-211. Apaydın, O. and Gönüllü, M. T. (2008) ‘Emission control with route optimization in solid waste collection process: A case study’, Sadhana, 33(2), pp. 71-82. Babu, A.G., Jerald, J., Noorul Haq, A., Muthu Luxmi, V. and Vigneswaralu, T. P. (2010) ‘Scheduling of machines and automated guided vehicles in FMS using differential evolution’, International Journal of Production Research - INT J PROD RES, 48(16), pp. 4683-4699 Bektas, T. and Laporte, G. (2011) ‘The pollution-routing problem’, Transportation Research Part B: Methodological, 45(8), pp. 1232–1250. Bertsekas, D. P. (1991) Linear Network Optimization: Algorithms and Codes, MIT Press, Cambridge, MA. Bodin, L. and Golden, B. (1981) ‘Classification in vehicle routing and scheduling’, Networks, 11(2), pp. 97-108. Bodin, L., Golden, B. Assad, A. and Ball, M. (1983) ‘Routing and scheduling of vehicles Boender, G.J., Raap, J., Prytulla, S., Oschkinat, H. and DeGroot, H.J.M. (1995) MAS NMR ‘structure refinement of uniformly enriched chlorophyll a water aggregates with 2D dipolar correlation spectroscopy’, Chemical physics letters, 237(5), pp. 502-508. Bunte, S. and Kliewer, N. (2009) ‘An overview on vehicle scheduling models’, Public Transportation, pp. 299-317. © 2017-2018 All Rights Reserved, No part of this document should be modified/used without prior consent Tutors India™ Your trusted mentor since 2001 I www.tutorindia.com UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX I UK # +44-1143520021, Info@tutorsindia.com

Castanon, D. (1993) Reverse Auction Algorithms for Assignment Problems, Algorithms for Network Flows and Matching, American Math.Soc. Clarkson, R. and Deyes, K. (2002) Estimating the social cost of carbon emissions, Goverment Economic Service Working Paper 140, Evanston: H.M. Treasury. Cordeau, J. F. and Laporte, G. (2003) ‘The Dial-a-Ride Problem (DARP): Variants, modeling issues and algorithms’, Quarterly Journal of the Belgian, 1, pp. 89–101. Daduna, J. R. and Paixao, J. M. P. (1995) ‘Vehicle scheduling for public mass transit an overview’, In: Proceedings of the Sixth International Workshop on Computer Aided Scheduling of Public Transport, pp. 76-90. Dantzig, G. B. and Ramser, J. H. (1959) ‘The truck dispatching problem’, Management Science, 6(1), pp. 80–91. Desrosiers, J., Dumas, Y., Solomon, M. M. and Soumis, F. (1995) ‘Time Constrained Routing and Scheduling’, In: Handbooks in Operations Research and Management Science, Amsterdam: Elsevier Science. Eckstein, J. (1988) Dual coordinate step methods for linear network flow problems, The College of Information Sciences and Technology, The Pennsylvania State University. Erdoğan, S. and Miller-Hooks, E. (2012) ‘A green vehicle routing problem’, Transportation Research Part E, 48, pp. 100-114 Forbes, M., Holt, J. and Watts, A. (1994) ‘ An exact algorithm for multiple depot bus scheduling’, European Journal of Operations Research, pp. 115-124. Freland, J. A. and Fortin, L. (1989), "Vehicle Scheduling with Sliding Time Windows", European Journal of Operational Research, 38, pp. 213-226 Freling, R., Wagelmans, A. P. M. and Paixão, J. P. M. (2001) ‘Models and Algorithms for Single-Depot Vehicle Scheduling’, Transportation Science, 35(2), pp. 165–180. © 2017-2018 All Rights Reserved, No part of this document should be modified/used without prior consent Tutors India™ Your trusted mentor since 2001 I www.tutorindia.com UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX I UK # +44-1143520021, Info@tutorsindia.com

Gerards, A. M. H. (1995) ‘Matching, in Network Models’, Ball, M.O., Magnanti, T.L., Monma, C.L. and Nemhauser, G.L. (eds.), NorthHolland, Amsterdam. Hasselstrom, B. (1980) Improved vehicle scheduling in public transport through systematic changes in the time-table , The College of Information Sciences and Technology, The Pennsylvania State University. Huisman, D., Freling, R. and Wagelmans, A. P. M. (2004) ‘A Robust Solution Approach to the Dynamic Vehicle Scheduling Problem’, Transportation Science, 38 (4), pp. 447-458 Jabali, O., Van Woensel, T. and De Kok, A. G. (2012) 'Analysis of Travel Times and CO2 Emissions in Time-Dependent Vehicle Routing', Production and Operations Management, 21, pp. 1060-1074. Jing-Quan Li, K. L. (2009) ‘Sustainability provisions in the bus-scheduling problem’, Transportation Research, pp. 50-60. Kliewer, N., Melloul, I. T. and Suhl, L. (2006) ‘ A time-space network based exact optimization model for multidepot’, European Journal of Operations Research, pp. 1616-1627. Kuo, Y. (2010) ‘Using simulated annealing to minimize fuel consumption fort the timedependent vehicle routing problem’, Computers & Industrial Engineering, 59, pp. 157-165. Lee, S. M. and Jung, H. J. (1989) ‘A multi objective production planning model in a ﬂexible manufacturing environment’, Int J Prod Res, 27(11). Lenstra, J. and Kan, A. (1981) ‘ Complexity of vehicle routing and scheduling problems’, Networks, pp. 221-227. Liu, C. (2013) ‘An Improved Adaptive Genetic Algorithm for the Multi-depot Vehicle Routing Problem with Time Window’, Journal Of Networks, 8(5).

Löbel, A. (1998) ‘ Vehicle scheduling in public transit and Lagrangian pricing’, Management Science, pp. 1637–1650. Mesquita, M. and Paixao, J. M. P. (1999) ‘Exact algorithms for the multi-depot vehicle scheduling problem based on multicommodity network own type formulations’, In: Wilson, N. H. (Ed.), Computer-Aided Transit Scheduling, Lecture Notes in Economics and Mathematical Systems, Berlin,Springer, pp. 221-243. Rardin, R. L. and Uzsoy, R. (2001) ‘Experimental evaluation of heuristic optimization algorithms: A tutorial’, Journal of Heuristics, 7(3), pp. 261–304. Ribeiro, C. and Soumis, F. (1994) ‘A Column Generation Approach to the Multiple Depot Vehicle Scheduling Problem’, Operations Research, 42, pp. 41–52. Scott, D. (1986) Minimal Fleet Size in Transhipment-Type Vehicle Scheduling Problems, quot Publication, Centre de Recherche sur les Transports, Univ. de Montral. Scott, D., Peeters, P. and Gössling, S. (2010) ‘Can tourism deliver its ‘‘aspirational’’ greenhouse gas emission reduction targets’, Journal of Sustainable Tourism, 18, pp. 393–408. Stockholm Environment Institute (1999) Costs and strategies presented by industry during the negotiation of environmental regulations, Stockholm: SEI, pp. 54. Slater, T.

(2006) ‘The eviction of critical perspectives from gentrification research’,

International Journal of urban and regional research, 30(4), pp. 737-757. Solomon, M. M., Baker, E. K. and Schaffer, J. R. (1988) ‘Vehicle routing and scheduling problems with time window constraints: efficient implementations of solution improvement procedures’, In: Golden, B. L. and Assad, A. A. (eds), Vehicle Routing: Methods and Studies, North-Holland, Amsterdam, pp. 85-105. Stern, N. (2006) The Economics of Climate change. The Stern Review, Cambridge University press, pp. 41-68. © 2017-2018 All Rights Reserved, No part of this document should be modified/used without prior consent Tutors India™ Your trusted mentor since 2001 I www.tutorindia.com UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX I UK # +44-1143520021, Info@tutorsindia.com

Sugawara, S. and Niemeier, D. A. (2002) ‘How much can vehicle emissions be reduced? Exploratory analysis of an upper boundary using an emission-optimized trip assignment’, Transportation Research Record, 1815, pp. 29-37. Suzuki, Y. (2011) ‘A new truck-routing approach for reducing fuel consumption and pollutants emission’, Transportation Research Part D, 16, pp. 73-77. Taniguchi, E., Thompson, R.G., Yamada, T. and van Duin, J.H.R. (2001) City logistics: network Modelling and Intelligent Transportation Systems, Pergamon, Amsterdam. Volgenant, A. (1996) ‘Linear and semi-assignment problems: a core oriented approach’, Computers and Operations Research, 23(10), pp. 917-932.

Wren, A. (2003) Scheduling vehicles and their drivers - forty years' experience,Tecnical report. Wygonik, E. and Goodchild, A. (2011) ‘Evaluating CO2 emissions, cost, and service quality trade-offs in an urban delivery system case study’, IATTS Research, 35, pp. 7-15.