The reduction of greenhouse gas emissions from freight transport by merging supply chains

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1 International Conference on Industrial Engineering and Systems Management IESM 2009 May 13-15, 2009 MONTREAL - CANADA The reduction of greenhouse gas emissions from freight transport by merging supply chains Shenle PAN 1, 2, Eric BALLOT 1, Frédéric FONTANE 2 1 MINES ParisTech, CGS- Centre de gestion scientifique, 60 Bd St Michel Paris Cedex 06, France 2 MINES ParisTech, CAOR- Centre de CAO-Robotique, Mathématiques et Systèmes 60 Bd St Michel Paris Cedex 06, France Abstract It is well known that freight consolidation is an effective way to improve the utilization of logistics resources. In fact, at present this policy is locally and fragmentally implemented at the operational level as it depends on the opportunities taken by carriers to consolidate freight. In this paper logistical mutualisation at the strategic level (merging supply chains) will be explored from the environmental point of view. With real data offered by two main French retail chains and through an optimization model, we are able to estimate the effect on reducing CO 2 emissions by merging supply chains. In addition, two transport modes, road and rail, are the considered in this paper. As regards the general dependency of the emissions produced by the modes of conveyance on their loads, the emission functions of the two modes are both piecewise linear and discontinuous functions. Therefore, by nature it is a MIP problem. The conclusions arrived at in this paper show that the logistical mutualisation proposed in this work is an efficient approach to reducing CO 2 emissions. At the same time, rail transport is an aspect that should be taken into account as well in achieving this objective due to its outstanding performance in terms of CO 2 emissions in some countries. Key words: Transportation, Merging supply chains, CO 2 Emissions, Mixed Integer Programming, Piecewise Linear Function. 1 Introduction In a context of a global economy and fierce competition, companies made intense use of transport to cope with the demands from their customers. On the other hand, it is well known that the climate change problem and the increasing price of energy have garnered worldwide attention over the past decades. As a significant source of greenhouse gas (GHG) emissions, the transport sector is naturally concerned by global warming. As a result, improving transport efficiency is among the foremost concerns of supply chain management (SCM) initiatives. Several studies show that today the lack of satisfaction regarding transport efficiency is based both on the use of vehicles and on greenhouse gas emissions, see [18, 20, 21]. From this standpoint, the consolidation of shipments was put forward and has been recently studied in several publications, see [3, 4, 9, 22]; and it has been shown that freight consolidation may be an efficient way to achieve lower costs and reduce inventories. In fact, at present this policy is locally and fragmentally implemented at the operational level by using a 3PL for example because it depends on the opportunities available to carriers to consolidate freight. As shown in [18, 20], despite the contribution of consolidation, the mean load factor of road transport is about 70% in general, which is consistent with the result of our data [2]. Thus, new approaches in logistical organization are called for. Furthermore, the achievement of economies of scale that are profitable for both suppliers and retail chains requires closer and longer term collaboration, see [11]. A new concept in this same area, the mutualisation of

2 freight consolidation between supply chains, namely merging supply chains, will be studied in this paper at the strategic level. As regards sustainable development, this paper aims to evaluate the effect of this mutualisation on reducing GHG emissions, especially CO 2 emissions. The new objective of reducing CO 2 emissions by optimizing the supply chain has not yet been widely studied in logistics. Currently, much more attention is paid to new fuel economy technologies applied to vehicles to reduce the environmental impact of freight transport. Nevertheless, concern by the freight transport sector should justifiably have been warranted. In the next part of our paper, we will take a look at the merging of two large retail chains supply chains in France. This second part will be followed by part 3 that details the preparation of data for the optimization model. Part 4 of the paper outlines the results and associated analyses with the conclusions following in the last part. 2 Methodology 2.1 Merging retail chains in France This project was developed in collaboration with a French association called Club Déméter ( that provided us with all the data for our research. This support guarantees that the database we worked on was indeed in agreement with the real network flow of some of the companies in the association. The data mainly consists of the flow of goods of two major French retail chains in the first 12 weeks of 2006, and also of the geographic location of both distribution centres of the two retail chains and the warehouses of their common suppliers. Based on the database offered, we propose that the merging of two supply chains can be achieved as follows. WH of Supplier i WH of Supplier j WH of Supplier k DC of Retailer m DC of Retailer n DC of Retailer l After merging WH of Supplier i WH of Supplier j / Upstream hub WH of Supplier k DC of Retailer m DC of Retailer n / Downstream hub DC of Retailer l Direct flows (before merging) Midstream flows Upstream flows Downstream flows Fig. 1. Example of the merging supply chains As illustrated in Fig. 1, originally the flows from the suppliers (i, j and k) warehouse or plant (WH) are directly shipped to retailers (m, n and l) distribution centres (DC). To achieve the merging of the supply chains, we create two kinds of hub in this network, an upstream hub and a downstream hub. Further, two assumptions are made to simplify the problem: (1) the hubs are respectively among the set of WH or DC in the database; (2) goods are allowed to be transported from WH to DC directly or to be consolidated at upstream hubs, downstream hubs or both. Hence, the merged network here is a three-echelon hub network, with the upstream path between WH and the upstream hub, the midstream path between the upstream hub and the downstream path between the downstream hub and DC. More details are discussed in the formulation section. The possibility of reducing CO 2 emissions obviously depends on the consolidation of a mass of flows that will be shipped together between the different supply chains in order to increase the truck load factor of each shipment.

3 2.2 Construction of the database IESM 2009, MONTREAL CANADA, May The database mentioned above consists of a large number of diverse goods flows (several thousands lines in Excel) concerning the most important 106 common suppliers of the two French retail chains. Initially, to be able to identically analyze the flows, they are all measured in number of pallets. This means that the flows are converted into quantities of equivalent full pallets. First the flows are subdivided into three classes according to the different types of products involved: CARE (pharmaceuticals, cosmetics/perfume and hygiene), GRO (grocery) and LIQ (liquids). The main reason for this subdivision is that these classes of product require different handling through the transportation due to their varying logistic behaviour. For example, a warehouse for liquid products will not have the same construction features as that for cosmetics. This classification in the French supply chains is maintained in this paper. According to the first study of this project [2] using the same data, it was shown that mixing flows with very different sizes should be avoided. Moreover, after the classification of products, the suppliers warehouses in each class are divided into three groups based on the size of flows in the warehouse, since the flows of each warehouse (even of the same supplier) are independently dealt with in our case. In order to obtain a balanced amount of variables in each group, the boundaries of the groups are respectively 0-200, and >600 pallet/week. Note that the flow per week involved here is an average of the flows covering the 12 week period studied. This decomposition doesn t exactly follow the real volume breakdown, but help the optimization process. It could be seen as a limitation of this work. Finally the initial problem is broken down into 9 subproblems in which the number of suppliers is showed in table 1. Table 1 The number of warehouses (or plants) in each group under classification CARE GRO LIQ Group pal< Group pal= Group pal> As mentioned above, the procedure of classification and grouping to reduce the extent of the problem, which also determines the framework of the database, is important to decrease the computational time of an optimization model presented in next part. 3 Problem formulation To be able to calculate and minimize CO 2 emissions from freight transport, we must first define an emission function according to mode of transport, as well as an optimization model. 3.1 Emission functions Two modes of transport, road and rail, are taken into account in this research. The emission function of their mode of transportation is based on several reports in this field, e.g. the MEET report [12, 15] and the COST 319 project [16]. Furthermore, CO 2 emissions factors in France applied to these functions can be found in some reports of the ADEME (Agency of Environment and Energy Management in France: see [8, 14, 21]. In these reports, CO 2 emissions are as a rule measured in terms of ton-km freight transported, which is also adopted in our case, but the ton-km is changed into pallet-km due to the form of the database. In this section, we simply indicate the final formulas to calculate emissions that are applied to the optimization model Road transport emissions First of all, the mode of road transport here refers to transport by HDV truck only (32-40 ton for general merchandise). According to the emission function for the truck given by [12, 14], some assumptions are made: (a) The average speed is 80 km/h; (b) The gradient of a road is not taken into account; (c) In general the truck

4 considered here is fully loaded with 25 tons for weight or 33 pallets for volume. Particularly, for the care and grocery classes, it is assumed that the truck is fully loaded at the same time by weight and volume (33 pallets weighting 25 tons). However for the liquid class which is doubtless heavier than the others, the truck is fully loaded only with 23 pallets weighing 25 tons. As a result, the final CO 2 emission function with the variable of load is: (1) where: : is the CO 2 emissions from a vehicle in kg/km with the variable of load x in pallet; : is the CO 2 emissions of a fully loaded (by weight) vehicle, which is = kg/km for HDV truck; : is the CO 2 emissions of an empty vehicle, which is = kg/km for HDV truck; c: is the volume capacity of a vehicle; as said above, which is 33 pallet/vehicle for the care and grocery classes and 23 pallet/vehicle for the liquid class Rail transport emissions According to the data from Fret SNCF ( 90% of the freight train locomotives in France are electrically driven, thus only the eclectically powered locomotive is considered here. The emission of air pollutant related to rail transport is calculated in two steps. The first step is the estimation of the energy consumption of a train in KJ per ton-km, which is required to move the train. After this step, the amount of emissions can be calculated according to the energy required. Consequently, the pollutant emission function related to energy consumption is as follows: (2) where: Ei: is the total emission of air pollutant i in kg; WSEC: is the weight specific energy consumption of the train in KJ/ton-km; Tkm: is the amount of freight transported by the train in ton-km; Tpt: is the load factor of the train, in ton-km/total train ton; BSEFi: is the brake specific emission factor in g/kwh of energy produced. Formula (2) which is corrected and validated by the main author of the MEET report [15] is the general form of calculation of air pollutant emissions. In this study, we focus on CO 2 emissions as the most representative of the emissions of electricity production. In France, according to the report of the ADEME [14], the BSEFco 2 of EDF in 2007 was 42 g/kwh. Some assumptions are also made here to simplifier the problem: (a) The average speed is 100 km/h; (b) The mean distance between stops is 100 km; (c) A train is composed of 13 wagons which is the minimum actual size for chartering a half full train; (d) Because of the large weight capacity of the wagon (56 ton/wagon), a wagon is only fully loaded by a volume equivalent to 36 pallets. The assumption (c) and (d) imply that the volume capacity of train here is 468 pallet/train. Similar to formula (1), formula (3) given below is used to calculate CO 2 emissions from rail transport. (3) where: : is the CO 2 emissions from a train in kg/km with the variable of load x in pallet; : is the CO 2 emissions of a fully loaded (by volume) train, which is = 0.96 kg/km for the care and grocery classes and = 1.16 kg/km for the liquid class; : is the CO 2 emissions of an empty train, which is = 0.498kg/km; c : is the volume capacity of a train; as said above, which is c = 468 pallet/train.

5 To compare the CO 2 emissions of these two transport modes, illustrated in Fig.2, it is assumed that one pallet weighs one ton, which means that the ton-km and pallet-km units are now equivalent. The conclusion, here very favourable to rail transport, should be considered with respect to the source of production of electricity that emits very little CO 2 /kwh in France. Fig. 2. Comparison of CO 2 emissions between road and rail transport Due to the upper integer part of x/c, functions (1) and (3) are in fact piecewise linear and discontinuous functions. That is also the foundation of the optimization model, which will be presented in the next section. 3.2 Optimization model Since the purpose of our optimization project is to minimize the CO 2 emissions related to freight transport in two large supply chains, the emission functions are adopted in the optimization model via an objective function. The discontinuity of the functions results in a Mixed Integer Linear Programming (MILP) problem in our case Formulization Croxton [5] cites three models, namely the IM (Incremental Model), the MCM (Multiple Choice Model) and the CCM (Convex Combination Model) to describe a formula for the piecewise linear function. Furthermore, according to this article and another one concerning the knapsack problem [17], these three MIP models for nonconvex piecewise linear minimization problems are equivalent. The MCM model is adopted in our case because it is adapted to the fact that the emission produced by freight transport in an arc depends on the sum of the flow of pallets on this arc and noted x. x Fig. 3. Variables for each emission segment As described in part 2, the merging supply chains in this case is carried out by way of a three-echelon network. Thus the problem actually involves minimizing the sum of CO 2 emissions of the three sections of transport (upstream, midstream and downstream). To simplify the problem, the flows would only be considered for one period and only one mode of transport would be involved. The optimization problem is stated as follows (for notation refer to the Fig.3):

6 with (5) Piecewise linear constraints, (6), (7), (8), (9) Flow-balance constraints, (10), (11), (12), (13), (14) Hub selection constraints, (15) where:, (16), (17), (18), (19), (20) Z: a constant large enough; K: set of commodities; and each has at least one specific origin and destination node that are respectively called warehouse and distribution centre; O, M, N, D: denote respectively the set of WH (source nodes), candidate upstream hubs, candidate downstream hubs, DC (destination nodes); Au, Am, Ad : subsets of arc set A, of which Au is the arc set on the upstream flow, Am on the midstream and Ad on the downstream. a: The set of arc subsets above ; dij: the distance of arc ; : piecewise linear function of CO 2 emissions related to the flow x; : quantity of commodity supplied at WH ; : quantity of commodity required at DC ; Sa: represents the number of segments on each ; s: piecewise linear segment on each ; : nonnegative fixed value of the intercept of segment on arc, in our case the piecewise function is identical on each arc, so the intercept of the same segment on every arc is always the same according to the mode of transport, such as for road transport and for rail;

7 : slope of segment on each arc. Because of the fact that all the segments are parallel, the slope here is constant with the same product and transport mode, such as for the care and grocery classes and for the liquid products in road transport. With regard to rail transport; similarly we have for care and grocery, and for liquid products; : binary variable on each arc, with if segment contains a nonzero flow, and otherwise; : lower and upper bounds of total flow lie in segment on each arc. In particular, for every arc we assume that and ; and also for each segment s, (which represents the capacity of conveyance depending on the pallet s density and area of the conveyance); : the total flow on arc if that flow lies in segment and ; and decision variables: : quantity of shipment of commodity on arc ; : binary variable, with if commodity supplied at WH is converged at hub upstream, and 0 otherwise; : binary variable, if DC is served by the downstream hub, and 0 otherwise; Constraints 6-10 which come from the MCM [5] assure that the total product flow on arc a must lie between the lower and upper bounds of that segment; and at most one segment contains a nonzero flow on each arc. Equations are the flow balance constraints in the three-echelon multicommodity network. Constraints correspond to the assumption that each supplier must have at most one related upstream hub to where his flow will be shipped; and each DC must be served by at most one downstream hub. This assumption is to simplify flow management at each stock point. In addition, if we take into consideration both transport modes (truck and train) in the model at the same time, the objective function, as well as the number of variables, is doubled, as a result of the selection between road and rail transport. Moreover, another additional constraint to bound the minimal flow by train exists only in the case of rail transport Approaches to reduce the optimization problem This MILP transportation problem is a NP-Hard problem[1], so the computing time of this problems could be influenced by its size. As a representative factor of the size of an instance of a problem, the number of variables is about in the biggest problem among the sub-problems presented. This leads us to explore the possibility of being able to reduce the size in order to solve the problem. Thus, in the model we made several assumptions as follows: a. As stated in part 3, the emission functions are nonconvex piecewise linear and discontinuous, and the number of segments depends on the size of flow. Concerning similar problems, some articles based on analyzing piecewise function efficacy indicate that some attributes of the function deeply influence computational time, e.g. the continuity and the number of segments, see [7, 17, 19]. Therefore, to simplify the function in road transport for example, we assume that a truck is fully loaded in an arc with a shipment above a volume equivalent to 5 trucks in one week. In other words, the maximum number of segments is set at 6 (so Sa=6 and s 6), in addition, the slope of the sixth segment is for the care and grocery classes, and for liquids. Due to a significant difference in load capacity between trucks and trains, the function in rail transport is reduced to 2 continuous segments, which represent the case that trains are always fully loaded above one train. The slope of the second segment in rail transport is for the care and grocery classes, and for liquids. b. According to Croxton [6], with the data in our case we can tighten the LP relaxation of the model by limiting the maximum bound of each product s flow in any arc in the network. c. Considering the fact that consolidation would not occur if the distance between WH and the upstream hub or between the downstream hub and DC is too great, a condition of distance in both upstream and downstream paths is introduced to filter candidate upstream and downstream hubs. In other words, the possible hubs are

8 situated within a certain radius from WH or DC. We studied the sensibility of the solution according to radius variation, and 100 km was shown to be the best compromise between computing time and solution quality Lower limit of emissions Aside from the model presented to minimize CO 2 emissions, it might also be valuable to calculate the lower limit of the emissions in transport, meaning that all shipments are direct from WH to DC with an absolutely fully loaded transport mean. As a result, equation (5) in the optimization model will be replaced by a completely linear objective function. The assumptions made allow all the sub-problems to be solved by coding and executing a computational programme. The results obtained are discussed in the next part. 4 Results and discussions The model presented is coded in ILOG s OPL 6.1 software with CPLEX 11.2 and run on Quad CPU Q6700 (2.66 GHz) hardware having 4 GB of RAM. The MIP problem is solved with OPL s default settings, except Memory Emphasis parameter to true to deal with the large size of the problems, refer to [13]. Furthermore, all the results obtained showed a variance of less than 3% between the best integer solution and the upper bound. Note that all the values exhibited in the following tables are measured in tons. It should also be recalled that, as stated in the methodology part, the suppliers in each class are divided in 3 groups: Group A < 200 pallet/week, Group B pallet/week and Group C > 600 pallet/week. 4.1 Road transport Initially, road transport is the exclusive transport mode involved in the optimization problem. In the framework of the 9 sub-problems presented, the purpose of this section is to evaluate the performance of merging supply chains in terms of CO 2 emissions (Emissions / merging in the tables) using the optimization model, by comparing results with the emissions from the actual transport system (Actual emissions in the tables). Additionally, the lower limit of emissions (Minimum emissions in the tables) will also be introduced as the optimum situation in which either the trains or trucks are absolutely fully loaded (linear emission function). Table 2 Emissions from road transport per week (ton CO 2 ) Groups of suppliers in CARE A B C Σ Actual emissions Emissions / merging Reduction Absolute Relative 45% 33% 9% 18% Minimum emissions Reduction Absolute Relative 78% 52% 17% 30% Groups of suppliers in GRO A B C Σ Actual emissions Emissions / merging Reduction Absolute Relative 49% 29% 6% 18% Minimum emissions Reduction Absolute Relative 78% 52% 15% 33%

9 Groups of suppliers in LIQ A B C Σ Actual emissions Emissions / merging Reduction Absolute Relative 49% 12% 5% 11% Minimum emissions Reduction Absolute Relative 79% 39% 10% 22% The results in the tables show that the approach of merging supply chains significantly reduces CO 2 emissions from transport, despite the fact that its gain in emissions is about half the maximum gain defined by the lower limit, namely the theoretical minimum emissions. Furthermore, it is obvious that the relative reduction in group C is much less than the other groups. This situation can be explained by the fact that at present the suppliers with massive flows logically make more effort to saturate their means of transport. Besides the reduction of CO 2, it is also important to indicate that the number of transport paths falls because of the mutualisation, as shown in the Fig.4. Source nodes Destination nodes Upstream hubs Downstream hubs Road transport Fig. 4. Example of transportation network before and after merging by road transport (CARE Group C) 4.2 Transport by the road and the rail This section aims to determine the effect of joint road/rail transport in reducing CO 2 emissions. This means that road and rail transport are alternative transport modes to be chosen according to the size of flows and their performance in terms of emissions. To reflect practical situations in transportation, a lower limit of flow for rail transport is fixed at 468 pallets which is a volume equivalent to half of a train of 26 wagons. Table 3 Emissions from joint road and rail transport per week (ton CO 2 ) Groups of suppliers in CARE A B C Σ Actual emissions Emissions / merging Reduction Absolute Relative 45% 53% 51% 50% Groups of suppliers in GRO A B C Σ Actual emissions Emissions / merging Reduction Absolute Relative 49% 32% 62% 52%

10 Groups of suppliers in LIQ A B C Σ Actual emissions Emissions / merging Reduction Absolute Relative 49% 15% 62% 53% According to the results in table 3, especially in group C, it can be concluded that joint road and rail transport is a significant way to reduce CO 2 emissions, provided the electrically powered train generates low emissions in France thanks to the low emission electricity production mainly nuclear and hydro. However, it should be noted that the problem of rail transport here is to obtain a (consolidated) flow that simultaneously meets the weekly demand of retailers and the minimum flow for rail transport (468 pallets for 13 wagons). That is to say that the weekly stock level of retailers can not be challenged. Comparing to the Fig.4, we can easily remark in the Fig.5 that the introduction of the rail transport simplifies the transport network and indicates that it is the means of transport of massive flows. Source nodes Destination nodes Upstream hubs Downstream hubs Road transport Rail transport Fig. 5. Example of transportation network before and after merging by joint transport (CARE Group C) 5 Conclusion This paper explored the effect of merging supply chains on reducing CO 2 emissions from transport with two possible modes, i.e. road and rail, in the context of a national distribution network of two major French retailer chains. We first pointed out the principal drawback of current freight consolidation, namely that it currently takes place in a local and fragmentary manner by using carriers and 3PL. This inspired us to search for another approach to achieve more efficient consolidation. In the aim of reducing CO 2 emissions from freight transport, we were interested in merging supply chains at the strategic level, an approach that demands further and longterm collaboration between the actors of supply chains (suppliers and retailers in this case) as defined in [10]. In this article geographical merging was proposed, namely geographical consolidation among suppliers or retailers. Moreover, an optimization model with a piecewise linear objective function was set up to quantitatively evaluate its impact on reducing CO 2 emissions. Benefiting from our association with Club Déméter, we tested the model with the real data of merchandise flows over 12 weeks in a large national distribution network composed of two French retailers supply chain. Due to the great size (more than 100,000 variables in all) of the problems, it was necessary to make some assumptions to reduce the problems size in order to obtain a solution with a gap of less than 3% in CPLEX. Finally, we obtained the results in tables 2 and 3, which globally yield a relative reduction of CO 2 emissions of 14% exclusively with road transport and of 52% with joint road and rail transport (with the French railway system described here). It should be recalled that all the results obtained in this paper are conditional upon an unchanged weekly service rate to retailers. Consequently, it can be concluded that merging supply chains as studied here provides a significant solution to reducing CO 2 emissions from freight transport. However, one must recall that certain factors present in actual supply chain situations such as the capacity of each stock point and arc, the empty truck runs and some other operational constraints, were not taken into consideration in this paper in the initial phase of our project. Apart from the environmental aspect, future research in the framework of this project could focus on an economic aspect concerning transportation and reloading costs. Furthermore, the impacts of logistical

11 mutualisation on shipment frequency, as well as inventory level and organization at stock points are also considerations of value that might be explored. 6 Acknowledgments Playing an indispensable role in our research, we gratefully acknowledge the support provided by the Club Déméter Association. The authors are also grateful to the R2DS program of the Ile-de-France Region that provided us with funding for this project. 7 References [1] R. K. Ahuja, T. L. Magnanti, and J. B. Orlin. Network flows: theory, algorithms, and applications. 1993: Prentice-Hall, Inc. Upper Saddle River, NJ, USA. [2] Eric Ballot and Frédéric Fontane. Reducing greenhouse gas emissions through the collaboration of supply chains: lessons from French retail chains. in International Conference on Information Systems, Logistics and Supply Chain Conference, Editior, Editor. 2008: Madison, Wisconsin. [3] James H. Bookbinder and James K. Higginson. Probabilistic modeling of freight consolidation by private carriage. Transportation Research Part E: Logistics and Transportation Review, (5): p [4] Raymond K. Cheung, Judy H. Tong, and Brian Slack. The transition from freight consolidation to logistics: the case of Hong Kong. Journal of Transport Geography, (4): p [5] K. L. Croxton, B. Gendron, and T. L. Magnanti. A Comparison of Mixed-Integer Programming Models for Nonconvex Piecewise Linear Cost Minimization Problems. Management Science, (9): p [6] K. L. Croxton, B. Gendron, and T. L. Magnanti. Models and Methods for Merge-in-Transit Operations. Transportation Science, (1): p [7] K. L. Croxton, B. Gendron, and T. L. Magnanti. Variable Disaggregation in Network Flow Problems with Piecewise Linear Costs. Operations Research, (1): p [8] C. De Boissieu. Rapport du Groupe de travail "Division par quatre des émissions de gaz à effet de serre de la France à l horizon 2050" sous la présidence de Christian de Boissieu vol. volume, 2006 [9] O. Ergun, G. Kuyzu, and M. Savelsbergh. Reducing Truckload Transportation Costs Through Collaboration. Transportation Science, (2): p [10] Julien Francois. Planification des chaînes logistiques : modélisation du système décisionnel et performance. in Université Sciences et Technologies. 2007, Bordeaux I Bordeaux. p [11] Bas Groothedde, Cees Ruijgrok, and Lori Tavasszy. Towards collaborative, intermodal hub networks: A case study in the fast moving consumer goods market. Transportation Research Part E: Logistics and Transportation Review, (6): p [12] J. Hickman, et al. Methodology for calculating transport emissions and energy consumption. TRL, Crowthorne, [13] O. P. L. ILOG. Studio 6.1: The User s Manual. in ILOG Inc [14] J. M. Jancovici. Bilan Carbone :Calcul des facteurs d émissions et sources bibliographiques utilisées vol. volume, ADEME, 2007 [15] M. W. Jorgensen and S. C. Sorenson. Estimating emissions from railway traffic. International Journal of Vehicle Design, (1): p [16] R. Joumard. Methods of estimation of atmospheric emissions from transport: European scientist network and scientific state-of-the-art. INRETS report LTE, [17] S. Kameshwaran and Y. Narahari. Nonconvex piecewise linear knapsack problems. European Journal of Operational Research, (1): p [18] J. Léonardi and M. Baumgartner. CO2 efficiency in road freight transportation: Status quo, measures and potential. Transportation Research Part D, (6): p [19] F. A. S. Marins, et al. Algorithms for network piecewise-linear programs: A comparative study. European Journal of Operational Research, (1): p [20] A. McKinnon, Y. Ge, and D. Leuchars. Analysis of Transport Efficiency in the UK Food Supply Chain vol. volume, 2003 [21] C. Rizet and B. KeÏTa. Chaînes logistiques et consommation d énergie: cas du yaourt et du jean. ADEME Project Report, Contrat INRETS/ADEME, [22] Jonah C. Tyan, Fu-Kwun Wang, and Timon C. Du. An evaluation of freight consolidation policies in global third party logistics. Omega, (1): p