Ultimate eng ela p ypsilantis fv

Freight Port - Hinterland Network Design: The Extended Gate Operator Perspective

Extended Abstract: The

16th

ELA Doctorate Workshop, Switzerland 2012

Author: Panagiotis Ypsilantis

E-mail: PYpsilantis@rsm.nl Supervisors: 1. Prof.dr. Rob A. Zuidwijk 2. Prof.dr Leo Kroon

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Introduction

The optimal design of freight transport networks has received a lot of attention from the academic world over the years. The contributions in the eld can be assigned to the greater class of problems on Network Design. On the other hand, the exclusive design of intermodal port-hinterland container transport networks has not grasped the attention of the academic world as much as it has of the business world.

Business

examples implementing the Extended Gate and Dryport concepts are gaining momentum, while academic contributions to support their implementation are limited since some of the main assumptions under the usual network design formulations do not hold under the implementation of such concepts.

In this research project we look to the dryport and extended gate concepts while trying to identify the main considerations for their quantitative modeling.

A gap in literature that should be lled has been

identi ed and a quantitative model representing the extended gate operator perspective in network design is presented and discussed. Last, a solution approach for this problem and some initial results are discussed.

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Dryports and Extended Gateways

The dryport concept has been presented in Roso et al (2009)[16]; that is that inland terminals are connected with the seaport terminals with high capacity transport means, like barges or trains and that containers are handled to the inland terminals as if they were to the seaports Veenstra et al (2012) [17] expanded the notion of dry ports to the the Extended Gate concept, while they identify the main bottlenecks for its implementation.

The authors identify the changing role of container terminals in this process while suggesting that the correct implementation of the extended gate concept can enhance the modal shift, logistics performance and regional development.

The latter concept suggests that the gates of the seaport container terminals are virtually

extended to inland locations, with high capacity corridors pushing high container volumes to inland terminals, immediately after their arrival in the seaport terminal, while postponing the customs and other added value activities to the inland terminals.. The direct results of the implementation of the Extended Gate concept is that all operations related with the congested parts of the main container terminal, such as stacking area, quay cranes and straddle carriers, are relieved and all processes are accelerated.

The shift to sustainable

transport modality, the economies of scale achieved and the enhancement of the seaport competitive position by the better hinterland connectivity are some of the indirect results of the implementation of such concepts.

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The inland dynamics are considered a driver of port development according to Notteboom and Rodrigue (2005) [14], who characterize this development phase as port regionalization , and identify hinterland accessibility as a main fundamental in the competitive position of a port. Container terminals are interested in more than their captive service regions and compete with neighbor ports on contestable hinterlands in order to attain a bigger share of container ows through their hinterland networks.

This is the case for almost

all seaports within the La Havre - Hamburg range, the hinterland of which is contestable and is serving a total of 350 million consumers.

The scope of this research is straightforward on pointing out the main

considerations under modeling the extended gate concept as interpreted in the concrete business models of seaport terminals.

There are several business examples that resemble the Extended Gate concept under di erent schemes in terms of governance and coordination of all processes involved.

We brie y describe the extended gate

business model as implemented by the Europe Container Terminals (ECT), the biggest container terminal in Europe which is located in Rotterdam. ECT has introduced the Extended Gateway Services (EGS) in 2009 with which shippers and shipping lines can transfer their containers to speci c inland terminals under the responsibility and customs license of ECT. These services are available for seven strategically chosen inland terminals which are owned and operated by ECT itself. These inland terminals are located in the hinterland of Rotterdam, three of them within the Netherlands, three in Belgium and one in Germany. ECT has attracts high ows of containers to destined to these inland locations and this can provide xed schedule connections by barges and trains to these destinations.

The frequency of connections ranges from 5 to 15 itineraries

per week. The connections between the marine and inland terminals are also controlled by ECT, and the transport is performed under terminal operator haulage scheme.

It is obvious that a Terminal Operating

Company (TOC), like ECT in this example, takes the role of both the seaport and inland terminal operator as much as the role of the transport provider among the inland and seaport terminals. There is a clear shift in the role of container terminals from being a node operator to being a ow operator as was also pointed out by Veenstra and Zuidwijk (2010) [18], while controlling a part of the total physical transport network.

This is going to be our de nition of the extended gate operator for the rest of this report.

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Modeling Gap in Service Network Design

The supply side of container transport networks, and in particular of intermodal networks, has been studied extensively in literature, but still contributions exclusively in the port-hinterland regime are limited. The former class of problems is well studied and is widely known as service network design problems, which are increasingly used to designate the tactical issues of carriers according to Crainic (2000) [7]. of the intermodal freight planning research is conducted by Caris et al.

(2008) [6].

An overview

The authors divide

the contributions in the eld according to the time horizon to strategic, tactical and operational models and according to the decision maker considered in each paper ranging among drayage operator, terminal operator, network operator and intermodal operator.

The main considerations and several models on intermodal

transportation are presented in Crainic and Kim (2007) [8], ranging from static service network design formulations, to location selection problems with balancing constraints and to Hub-location problems etc.

The extended gate concept as implemented in the business world challenges the main assumptions underlying the most common service network design modeling contributions. The main assumptions are that each part of the network is operated individually in case we consider the carrier perspective and that the total network is controlled by a single authority in case we consider the network operator perspective. The competitive environment in which these business models have to be implemented and designed from a strategic perspective is missing from literature. The vertical integrated role of container terminals in controlling a part of the total physical network and nally the ability of the proposed intermodal transport solutions to attract container ows given their discriminated characteristics when considering the alternative transport solutions have to be extensively considered.

The trade-o between customer demand characteristics and

carrier strategies is supposed to lead to the development of a variety of possible inland container routing patterns as pointed out by Notteboom (2008) [13]. Of course the e ective execution of the extended gate concept, is dependent on the amount of container ows that will be attracted to go through the extended gates, since economies of scale dominate the cost structure of operating the connections between the seaport and inland terminals.

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4

Network Design: The Extended Gate Operator Perspective

Considering the network design problem from the extended gate operator's perspective in a strategic level, we can identify among others three major decision areas. First, the locations or inland terminals that the gates of the seaport terminals should be extended must be determined. Second, the frequency of the barge and train connections between the inland and seaport terminals should be determined.

The frequency of

connections is a variable that changes the attractiveness of the extended gates to container ows, if one considers the value of time and reliability on container transport, in addition to economies of scale that will take place for higher frequencies.

Third, the price that the extended gate services will be o ered to

clients will also in uence the amount of container volumes transferred through the extended gates. In order to e ciently model the above more e orts should be put on modeling the demand side of port hinterland container transport. The container ows for speci c regions should be discriminated according to their cargo type, value of time, security and sustainability requirements.

The extended gate operator then should introduce the network design and extended gate services that would attract a share of the ows in a competitive environment with alternative routing and transportation solutions. The routing selection and the amount of ows passing though each node and arc of the network should depend on the shippers taking into account the total logistics costs criterion. In addition, these models should be formulated based on the so called triple bottom line perspective, by simultaneously evaluating economic, environmental and social performances of port-hinterland logistics systems.

We suggest that the most appropriate formulation for the extended gate operator perspective in network design problem in a strategic level would be a bi-level mathematical programing model as follows. In the rst level the pro ts of the extended gate operator should be maximized by the selection of the nodes and links, frequency of connections, pricing structures and services. In the second level the total logistics costs faced by the users of the network should be minimized by the determination of the the container ows through the total transport network, consisting also of competitive to the extended operator nodes and links.

In

this formulation the decision variables are determined simultaneously in order to satisfy both constraints, of course with a priority on the rst level and is equivalent to well known Stackelberg Game, in game theory, in which the leader makes his decision and waits for the followers to react on his decision.

The rst step in formulating the network design problem from the extended gate operators perspective has been done and is presented in Appendix I. In this simpli ed version of the model we consider the prices for the extended gates the selection of the inland terminals that will act as extended gates and indirectly the frequency of connections as the main decision variables of the rst level, the selection of which aims to maximize the E.G. operator pro ts.

This pro t consists of handling costs charged in the seaport and

inland terminals, for containers not using the extended gates, and a at cost(consists of both handling and transportation costs) charged for using the extend gates. The cost of operating the extended gates is subtracted and is represented by a piecewise linear function to represent ow dependent economies of scale.

Economies of Scale

Economies of scale are usually incorporated in Hub and Spoke network formulations. Most of these contributions apply a discount factor

a, 0 ≤ a ≤ 1,

to the transportation costs between any two of the selected

nodes of the network that will act as hubs. It is clear that this simplistic approach to economies of scale do not take into account the nal amount of ow that will pass through the interhub link, so most of the researchers when apply this method post-assess and post-validate their solutions in comparison to their assumptions when determining the discount factors they used. Some of the literature contributions that use this simplistic approach can be found in Kimms (2006)[11].

It has been stated by some researchers that assuming that these discount factors are independent of the ows can lead to false hub allocations and result interpretation. That is why there should be a shift on using ow dependent economies of scale. O'Kelly and Bryan (1998)[15] propose to use the Bureau of Public Roads (BPR) function (She (1985)) to depict the economies of scale. (Horner and O'Kelly (2001) [10], Klincewicz (2002)[12], O'Kelly and Bryan (1998)[15]. This is a continuous increasing function for increasing ows which yields decreasing costs per unit transported for increasing ows. This function can be approximated by a piecewise linear function that enables the e ective solution of the mathematical problems that incorporate this approach.

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Figure 1: Economies of Scale Structures

Kimms (2006)[11] discusses the two di erent approaches of modelling economies of scale in hub and spoke networks and proposes alternative formulations. It is argued that the rst approach with a xed discount factor is wrong while the second approach with ow dependent economies of scale as described previously would be valid if the transportation is performed by a third party. In the proposed formulation the economies of scale are formulated with anon continuous piecewise linear function with high xed costs for setting the capacity of the links and capacity independent variable costs for the units moved through the network. It should be noted that in the latter case the cost per unit is only a decreasing function of the ow for every capacity plan (for every line) but can be higher depending on utilization of the decided capacity of the link.

We agree in principle with these arguments especially when models are in the operational or tactical level but we argue that in a strategic level the economies of scale as proposed by O'Kelly and Bryan (1998)[15] are the most appropriate since a constantly decreasing cost per unit transported should apply.

The structure of the economies of scale is presented in Figure 1. This formulation of the economies of scale di ers from the one proposed in O'kelly and Bryan (1998)[15] in the sense that there is a positive xed cost for opening the link instead of the approximation of the BRP function which starts at costs for zero ows. On the other hand this approximation of the economies of scale is di erent from the one proposed by Kimms (2006)[11] since this is a convex continuous piecewise liner function.

Relevant Modeling

Brotcorne et al (2000)[2] introduce the the freight tari setting problem in which the objective is to maximize the the revenues of carrier controlling a set of arcs of the network by setting the tari s for using these arcs, while the ows over the network are determined in the second level minimizing the total transport costs faced by the users of the network. This is the simplest formulation since all terms are continuous and the solution methodologies lie on the reformulation of the problem into a single level bilinear formulations with disjoint constraints which are solved with heuristics based on the primal-dual heuristic proposed by Genreau (1996). Brotcorne et al (2001)[3] extend their previous work by considering a multicommodity network in which the leader maximizes his revenues by setting the tolls on the set of arcs he controls. In this setting again a primal-dual based heuristic is used with an extension that forces tolls applied for each commodity to be equal. An exact algorithm for solving the pricing problems on a network by partially and e ciently generating candidate solutions is presented in Brotcorne et al (2011)[1] while a tabu search algorithm is

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presented in Brotcorne et al (2012).

Brotcorne et al (2005) [5] further extend their work by considering the joint pricing and capacity setting problem in a multicommodity transportation network. This problem is formulated also formulated as a mixed integer bi-level and solved using a primal-dual base heuristic. The last two models somehow incorporate the tradeo s between revenues and costs generated for the leader when designing his network; it is stated that until then these issues were treated separately although they are intrinsically linked and should be treated jointly.

The economies of scale principle is assumed to be satis ed by considering that the unit cost of increasing capacity is decreasing.

Brotcone et al (2008)[4] rst consider the joint pricing and design of the network

by assuming investment xed costs applied to the leader for operating and arc of the network. This case is formulated as mixed integer bi-level program with binary decision variables indicating whether or not an arc is used in a multicommodity transportation network. In addition primal-dual methodology commonly used, a novel Lagrangian relaxation methodology is used to incorporate the binary design variables in the solution methodology. The solution methodology is based on the primal-dual algorithm proposed by Gendreau et al (1996) [9]while langragian relaxations is implemented to determine the binary design variables.

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Conclusions and Further Study

The extended gate and dryports concepts are brie y presented and a business example implementing the extended gate concept is presented.

The main considerations for modeling the extended gate concept in

network design and the identi cation of the present gaps in literature are stated. Moreover, a rst modeling step in the proposed direction is performed with our model.

Our research is still in an initial phase and several extensions of the model should be eventually considered.

Our rst extension would be to enable the location selection of the inland terminals. Another extension will be the mulch-commodity case that will consider di erent characteristics of containers in the same O-D pairs and then e orts will be put on incorporating time dimensions in our model. In addition to these extensions, solving the model for some realistic instances would give great insights on how these port-hinterland networks or extended gates should be designed.

References [1] L. Brotcorne, F. Cirinei, P. Marcotte, and G. Savard.

An exact algorithm for the network pricing

problem. DISCRETE OPTIMIZATION, 8(2):246 258, MAY 2011.

[2] L. Brotcorne, M. Labbe, P. Marcotte, and G. Savard.

A bilevel model and solution algorithm for a

freight tari -setting problem. Transportation Science, 34(3):289 302, 2000.

[3] L Brotcorne, M Labbe, P Marcotte, and G Savard. A bilevel model for toll optimization on a multicommodity transportation network. TRANSPORTATION SCIENCE, 35(4):345 358, NOV 2001.

[4] L. Brotcorne, M. Labbe, P. Marcotte, and G. Savard. Joint design and pricing on a network. Operation Research, 56(5):1104 1115, 2008.

[5] L. Brotcorne, M. Labbe, P. Marcotte, and M. Wiart. Joint pricing and network capacity setting problem.

2005.

[6] A. Caris, C. Macharis, and G.K. Janssens. Planning problems in intermodal freight transport: Accomplishments and prospects. Transportation Planning and Technology, 31(3):277 302, 2008.

[7] T.G. Crainic. Service network design in freight transportation. European Journal of Operational Research, 122(2):272 288, 2000.

[8] T.G. Crainic and K.H. Kim.

Chapter 8 intermodal transportation.

In C. Barnhart and G. Laporte,

editors, Transportation, volume 14 of Handbooks in Operations Research and Management Science, pages 467 537. Elsevier, 2007.

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[9] M Gendreau, P Marcotte, and G Savard.

A hybrid Tabu-ascent algorithm for the linear bilevel pro-

gramming problem. JOURNAL OF GLOBAL OPTIMIZATION, 8(3):217 233, APR 1996.

[10] Mark W. Horner and Morton E. O'Kelly. Embedding economies of scale concepts for hub network design.

Journal of Transport Geography, 9(4):255 265, 2001.

[11] Alf Kimms.

Economies of scale in hub and spoke network design models: We have it all wrong.

In

Perspectives on Operations Research, pages 293 317. 2006.

[12] John G. Klincewicz. Enumeration and search procedures for a hub location problem with economies of scale. Annals of Operations Research, 110:107 122, 2002. 10.1023/A:1020715517162.

[13] T.E. Notteboom.

The relationship between seaports and the intermodal hinterland in light of global

supply chains: European challenges. 2008.

[14] T.E. Notteboom and J.P. Rodrigue. Port regionalization: towards a new phase in port development.

Maritime Policy Management, 32(3):297 313, 2005.

[15] M.E OKelly and D.L Bryan. Hub location with ow economies of scale. Transportation Research Part B: Methodological, 32(8):605 616, 1998.

[16] V. Roso, J. Woxenius, and K. Lumsden. The dry port concept: connecting container seaports with the hinterland. Journal of Transport Geography, 17(5):338 345, 2009.

[17] A. Veenstra and Zuidwijk R.A.

The extended gate concept for container terminals:

Expanding the

notion of dry ports. Maritime Economics and Logistics, 14(1):14 31, 2012.

[18] A. Veenstra and R.A. Zuidwijk. 17.the future of seaport hinterland networks. In Liber Amicorum, In memoriam Jo van Nunen, pages 205 215. Dinalog, 2010.

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Appendix I: Freight Network Design with Pricing and Economies of Scale (FNDPES) Let us consider an underlying network

G = (N, A)

with node set

N

and and arc set

A.

A node can be a

supply or demand node or a transhipment node in case it represents a deepsea or inland terminal. The set of

A1

arcs is partitioned in three subsets; the set

represents the candidate extended gates, the set

A2

represents

all arcs originating from the deepsea and inland terminals which implements the extended gate concept and

A3 represents all other arcs operated by competitive carriers.

S T C ij ∀ij ∈ A A and the container handling costs at the tranship2 3 S HC ij ∀ij ∈ A2 A3 . The demand or supply of a node is given by dj ∀j ∈ N

are operated by competitive carriers and the set The transportation costs are given ment nodes are also give as

and is negative for supply nodes positive for demand nodes and equal to zero for transhipment nodes. The economies of scale are represented by piecewise linear functions consisting of|

Q |lines

denoted by

q ∈ Q.

Every line can be represented by its intercept, which stands for the xed costs associated with operating a speci c link

ij

under a speci c cost structure

ij

variable costs of moving one unit in a link

q , F C ijq ∀ij ∈ A1 , ∀q ∈ Q

and its slope, which stands for the

under a speci c cost structure

q , V C ijq ∀ij ∈ A1 , ∀q ∈ Q .

T ij ∀ij ∈ A1

The decision variables of our model can be partitioned in three main groups. First, the tari s

are modelled as the price per unit transferred through the extended gates; this is used to determine part of the revenues in the rst level while is determining part of the costs in the second level. Second the decision ow variables,

Y ij ∀ij ∈ A1 , X ij ∀ij ∈ A2

and

Z ij ∀ij ∈ A3

which are formulated as the amount of ows

going through each corresponding arc. Finally, the auxiliary variables variable

y ijq ∀ij ∈ A1 , ∀q ∈ Q.

Rijq ∀ij ∈ A1 , ∀q ∈ Q

and the binary

These variables are associated to determine which line from the economies of

scale structure should be considered. Moreover, the binary

P yijq

indicates if and extended gate in link

ij

is

q open or not.

(1)

The rst level objective

represents the net revenues of the TOC which acts as an E.G. operator and

consist of the revenues from the use of the extended gates, the revenues of handling containers in the deepsea and inland terminals operated by the leader while these revenues are diminished by the costs of operating the extended gates when considering economies of scale. Constraints

(2)& (3)

guarantee that the economies

of scale are correctly considered.

The second objective

(6)

represents the total transportation and handling costs faced by the users of the

network. These costs consist of the service costs generated of containers passing through the extended gates of the handling costs of all containers on the transhipment nodes and of all transportation costs on every arc.

Constraint

(7)

stands for the ow constraints.

max

T,R,y

X

T ij Yij +

ij∈A1

X

HCij Xij −

ij∈A2

X X

(F Cijq yijq + V Cijq Rijq )

ij∈A1 q∈Q

s.t.

X

Rijq = Yij ∀ij ∈ A1

(1)

q

X

arg min

X,Y,Z

ij∈A1

X i∈N

Rijq ≤ yijq M F ∀ij ∈ A1 , q ∈ Q

(2)

yijq ∈ {0, 1} ∀ij ∈ A1 , q ∈ Q

(3)

Rijq ≼ 0 ∀ij ∈ A1 , q ∈ Q

(4)

T ij Yij +

X

(HCij + T Cij )Xij +

ij∈A2

(Xij + Yij + Zij ) −

X

(HCij + T Cij )Zij

ij∈A3

X

(Xji + Yji + Zji ) = dj ∀j ∈ N

i∈N

Xij , Yij , Zij ≼ 0 ∀ij ∈ A1 , A2 , A3 , q ∈ Q

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(5)