An integrated logistics model for environmental conscious

Transcription

1 An integrated logistics model for environmental conscious suly chain networ design An integrated logistics model for environmental conscious suly chain networ design Amin Chaabane Deartment of Automated Manufacturing Engineering École de Technologie Suérieure Montreal, Quebec, Canada H3C 1K3 Marc Paquet Deartment of Automated Manufacturing Engineering École de Technologie Suérieure Montreal, Quebec, Canada H3C 1K3 ABSTRACT Amar Ramudhin Deartment of Automated Manufacturing Engineering École de Technologie Suérieure Montreal, Quebec, Canada H3C 1K3 Mohammed Amine Benaddour Deartment of Automated Manufacturing Engineering École de Technologie Suérieure Montreal, Quebec, Canada H3C 1K3 Oerations Research has addressed a variety of environmental roblems outside the traditional suly chain management area such as remanufacturing, reverse logistics, and waste management. Suly chain sustainability, which includes designing green suly chains, will gain much more attention in the future. Indeed, most comanies are still in the early stage of considering a green initiative. Traditionally, otimization models for suly chain networ design looed to different strategic networ alternatives, and analyze the trade-offs between logistics costs and service requirements. Today, with the strong emhasis in reducing greenhouse gas footrint, the integration of such consideration into the suly chain networ design hase will rovide to comanies much more visibility on how to manage efficient, effective, and green suly chains. In this aer, a mathematical rogramming model for environmental conscious suly chain networ design is introduced with the exlicit inclusion of carbon emission cost. By considering the greenhouse gases emissions cost together with traditional logistics costs, the roblem is formulated as a single objective otimization rogram. The methodology uses mixed integer linear rogramming modeling technique to deal with different strategic decisions, including sulier and subcontractor selection, roduct allocation, caacity utilization, and assignment of transortation lins required to satisfy maret demand. This new formulation rovides decision maers with a quantitative decision suort system to understand the tradeoffs between the total logistics cost and the carbon footrint reduction. Keywords Suly chain management, networ design, greenhouse gas (GHG) emission, CO 2 emission, mixed integer rogramming. INTRODUCTION Climate change and the environment are some of the biggest issues facing the world today. Greenhouse gases (GHG) emissions, articularly carbon dioxide (CO 2 ), are the main contributing factor of global warming (Intergovernmental Panel on Climate Change, 2007). The Intergovernmental Panel on Climate Change (IPCC), a scientific body tased to evaluate the ris of climate change, reorted in the Fourth Assessment Reort that: global GHG emissions due to human activities have grown since re-industrial times with an increase of 70% between 1970 and 2004 (Intergovernmental Panel on Climate Change, 2007). The annual emission of CO 2 grew about 80% between 1970 and 2004 (see Figure 1). Also, the environment has been a constant reoccuation in the global media, and now governments are taing real actions in resonse to the emergence of green and environmental conscious strategies and international regulations (Kyoto rotocol). Indeed, in 2002, the resident of United States (U.S) announced a climate olicy to reduce GHG intensity by 18 ercent over the next decade. The Environmental Protection Agency (EPA), an agency of the federal government of the U.S, is charged for heling to achieve this goal, and collaborate with rivate and ublic organizations. In the recent EPA s strategic lan, the agency announced that by 2012, 160 Million Metric Tons of Carbon Equivalent (MMTCE) of emission will be reduced through EPA s voluntary climate rotection rograms (99 MMTCE will be reduced in the industry sector and 15 MMTCE will be reduced in the transortation sector) (U.S. Environmental Protection Agency s (EPA), 2006). Moreover, the government of Canada lans to regulate both GHG emissions and air ollutants. The action will imose mandatory targets in terms of GHG emissions reduction for some industries to achieve a goal of an absolute reduction of 150 mega tons in GHG emissions by 2020 (ecoaction, 2007). Proceedings of the Fourteenth Americas Conference on Information Systems, Toronto, ON, Canada August 14 th -17 th

2 An integrated logistics model for environmental conscious suly chain networ design So, it is not surrising to see that Cororate Social Resonsibility (CSR) and green initiatives are on the rise (from 45% of Global fortune 250 comanies in 2002 to 67% in 2005) 1. A number of organizations have already made the move and they are lessening their harmful imact on the environment while reducing different logistics costs. For examle, Texas Instruments saved USD 8 million each year by reducing its transit acaging budget for its semiconductor business through source reduction, recycling, and use of reusable acaging systems. Figure 1. Global anthroogenic GHG emissions (source : (Intergovernmental Panel on Climate Change, 2007)) With all this global attention, the concet of green suly chain management (GSCM) is gaining increasing interest among researchers and ractitioners of suly chain management area. The scoe of GSCM ranges from reactive monitoring of the general environment rograms to more roactive ractices imlemented through various oerations ractices: reduce, reuse, rewor, refurbish, reclaim, recycle, remanufacture, and reverse logistics. A recent good review can be seen in the aer of (Srivastava, 2007). It is imortant to notice that green and environmental suly chain management literature had been largely develoed with integration of reverse logistics and waste management activities. Nevertheless, until now we are not able to quantify clearly the real imact of such imrovements relative to GHG emissions reduction. Some recent articles had addressed the roblem (Ferretti et al., 2007; Hugo et al., 2005). This was motivated esecially by the major advancement of ractical methodologies to estimate recise GHG emissions of several suly chain activities and relative to different industrial contexts (Intergovernmental Panel on Climate, 2006). Five years ago, GHG footrints measurement may have seemed strange, but today it is commonlace. Many sources of information including databases are now available for estimating GHG emissions such as the IPCC Emission Factor Data Base (EFDB), the EMEP/CORINAIR Emission Inventory Guideboo, the International Emission Factor Database (OECD), etc. In the same direction, we focus in this article on develoing an integrated logistics mathematical model for green suly chain networ design with GHG emissions considerations. The environmental imact is measured through CO 2 emissions caused by transortation activities within the suly chain. The roblem is mathematically formulated as a mixed integer linear rogram (MILP). Indeed, to be considered environmentally resonsible, the suly chain has to include a new cost related to the quantity of GHG emissions. This cost can reresent taxes or cost to buy carbon credits from a carbon maret. The remaining of the article is as follows. After an introduction to the roblem context, the literature review about GSCM is detailed in section 2. An integrated framewor for sustainable suly chain management is roosed in section 3. Section 4 resents the mathematical model formulation for environmental conscious suly chain networ design. The sets, indices, arameters, major decision variables, objective function and the different constraints are also exlained in details. Section 5 demonstrates via an illustrative numerical examle how the model can be used to evaluate otential reduction of GHG emissions and the direct imact on suly chain configuration and costs. Finally, the conclusion and future extensions to the model are discussed. 1 Source : KPMG International Survey of Cororate Resonsibility reorting in 2005 Proceedings of the Fourteenth Americas Conference on Information Systems, Toronto, ON, Canada August 14 th -17 th

3 An integrated logistics model for environmental conscious suly chain networ design LITTERATURE REVIEW Traditional suly chain management ractices identify certain erformance measures which are the drivers in the evaluation of suly chain effectiveness and efficiency. Tyically, they were concerned with: (1) customer satisfaction, service level, or resonsiveness and (2) cost/rofit. But today, in resonse to more rigid environmental regulations and changes in environmental suly chain conscious, there has been a need to develo guidelines and standards to assist suly chain mangers to consider the imact of their decisions on the environment, nown as Green Suly Chain Management (GSCM). Different studies available on this subject are summarized in a recent review by Srivastava (2007). It is not surrising to see that mathematical modeling based methodologies are the most common aroaches used to tacle GSCM roblems (Srivastava, 2007). Indeed, these models can be embedded as decision suort systems (DSS) for GSCM. DSS roved their efficiency to manage traditional suly chain networs nown today as advanced lanning and scheduling systems (APS) rovided bye comanies such as i2, Manugistics, ILOG, and SAP. We believe that analytical methods will continue to contribute actively for the develoment of GSCM ractices. A variety of mathematical tools and techniques have been used to tacle roblems related to GSCM such dynamic rogramming, non-linear rogramming, Marov chains, and multi-criteria decision maing. Different solution rocedures were roosed and vary from exact solutions using linear rogramming (LP) solver such as LINGO to heuristic based solutions. Here, we give a detailed review about LP methodologies alied to different GSCM contexts. Very early, Bloemhof-Ruwaard et al. (1996) studied the roblem of aer recycling and how it reduces the environmental imact of the Euroean ul and aer sector. They used a model based on LP to analyze scenarios with different recycling strategies. Sengler et al. (1997) develoed two mathematical models for two lanning roblems: recycling of industrial byroducts and dismantling and recycling of roducts at the end of their lifetime. The models have been alied to large industrial case studies in the fields of recycling of demolition waste and by-roduct management in the steel industry. Barros et al. (1998) roose a two-level location mathematical model for the recycling of construction waste. The model was solved using a heuristic rocedure. The model was alied for the sand recycling networ in the Netherlands and shows the utility of this aroach. Giannios (1998) resented a multi-objective linear model for locating disosal or treatment facilities and transorting hazardous waste of a transortation networ. Jayaraman et al. (1999) develoed a mixed integer rogramming model for the suly chain networ design roblem while incororating the location of remanufacturing/distribution facilities, the transshiment, roduction and stocing of the otimal quantities of remanufactured roducts. Fleischmann et al. (2001) resented a facility location model while considering a closed-loo suly chain. The roosed model shows how roduct return flows have a real imact on the logistics networ and deend largely from the context studied. Luo et al. (2001) resented a mathematical model to design and otimize suly chains in terms of different erformance indices such as roduct cost, cycle time, quality, energy and environmental imact in the context of global and Internet-based manufacturing. A multi-objective otimization model was formulated and solved for a ersonal comuter manufacturer. Sheu et al. (2005) resented a linear multi-objective rogramming model that otimizes the oerations of an integrated suly chain while incororating reverse logistics activities for used-roduct. Aling the roosed aroach for a noteboo comuter manufacturer suly chain, analysis reort an imrovement of the net rofit by 21.1%. Hugo and Pistiooulos (2005) resented a mathematical model for designing and lanning the suly chain. They include life cycle assessment (LCA) rinciles in the classical lant location and caacity exansion roblem. The environmental erformance was monitored using the Eco-Indicator 99 method. Jayaraman (2006) resented a mathematical linear rogramming model for aggregate roduction lanning and control. The major decisions are the determination of the number of units of core tye with a nominal quality level that is disassembled, disosed, remanufactured and acquired in a given time eriod. Data from a comany that remanufactures mobile telehones are used to validate the model. Lu et al. (2007) resented, in a recent study, a method using some rocedures to evaluate the effectiveness of rojects sulying green suly chain concet. A multi-objective decision maing rocess for green suly chain management is considered to hel the suly chain manager in measuring and evaluating suliers' erformance based on an Analytical Hierarchy Process (AHP) method. Ferretti et al. (2007) roosed a model to evaluate the economic and environmental effects of the industrial ractice case study. The outut of the model was the determination of the suly aluminum mix caable of balancing the economic benefits versus the environmental requirements. Finally, Sheu (2008) builds on the concets of GSCM and resents a multi-objective otimization rogramming aroach to address the issue of nuclear ower generation. A linear multi-objective otimization model is formulated to otimize the oerations of the nuclear ower generation and the reverse logistic flows of the roduced waste. As we see, GSCM has been studied much more from recycling and waste management ersective rather than a global and integrated suly chain networ design oint of view. However, increased regulations and governmental ressure in many countries to reduce their carbon footrints associated with the introduction of the carbon trading maret have motivated Proceedings of the Fourteenth Americas Conference on Information Systems, Toronto, ON, Canada August 14 th -17 th

4 An integrated logistics model for environmental conscious suly chain networ design comanies to measure their emission and redesign the suly chain. As a consequence, it is clear that there is a real need for an integrated methodology to be able to measure carbon footrints and identify different scenario to analyze by mangers in order to minimize costs while considering a green business strategy. The develoment of advanced decision suort systems that integrate strategic and oerational decisions with the carbon footrint emission measurement as an additional ey erformance indicator is imortant. This will hel suly chain managers to trade-off the imact of environmental imact decisions on both cost and service level when lanning suly chain oerations. Traditional studies on strategic suly chain management concet treat such consideration in the suly chain networ design hase. We believe that efficient GSCM must begin with environmental conscious suly chain networ design and this is the subject of the roosed aroach. TOWARDS A GREEN SUPPLY CHAIN MANAGEMENT FRAMEWORK Suly chain management is better understood within the context of end-to-end ey rocess deicted in Figure 2 and adated form the Suly-Chain Oerations Reference-model (SCOR Model) (Suly Chain Council, 2006). The whole activities can be aggregated into different critic areas: lan, source, mae, store, transort, and reverse logistics. GSCM begins with an otimization of these major ey rocesses and targets sustainable suly chain lanning, sustainable rocurement, sustainable manufacturing, sustainable storage, sustainable transortation, and sustainable reverse logistics. GSCM has to consider exlicitly financial, environmental and social imacts of suly chain activities. Financial benefits can be measured via revenue increase, cost reduction, increased asset utilization and customer service enhancement. Environmental benefits can be monitored via the reduction in fuel consumtion, reduction in GHG and water emissions, increase of energy efficiency use and waste reduction. For the social benefits, this can be seen by the reduction of noise, traffic congestion and imrovement of quality of life (health and safety). It is clear that considering all rocesses in the same model is imossible. But it is essential to tae into account the ey imacts when tacling any rocess. Figure 2. A strategic framewor for green suly chain management ractices The lan rocess contains activities erformed at the strategic level. It includes roduct lifecycle management (PLM) and suly chain networ design otimization. In one hand, life cycle management taes into account that roducts need to be managed through design, roduction, oeration, maintenance and end of life reuse or disosal. Product design and acaging influence the efficiency and effectiveness of the suly chain activities, and later logistics cost, waste and GHG emissions. In the other hand, Suly chain networ design, which is the scoe of the roosed mathematical model, is the second imortant decision in the lan rocess. Indeed, cometitive marets, ressure to reduce inventory and costs, merger activities, rising energy and fuel costs are the most common incentives for a cororate to examine the suly chain networ and define the number, tye, location of manufacturing and distribution facilities and the transortation channels and modes used to serve customers. Including environmental and social imacts with the traditional financial imact allow comanies to reduce the harmfulness to the environment while still achieving the strategic financial targets. Proceedings of the Fourteenth Americas Conference on Information Systems, Toronto, ON, Canada August 14 th -17 th

5 An integrated logistics model for environmental conscious suly chain networ design MATHEMATICAL MODEL FORMULATION FOR GREEN SUPPLY CHAIN NETWORK DESIGN Model descrition Green suly chain networ design must integrate the additional ey factors described before. In this aer we concentrate on the integration of CO 2 emission when designing the suly chain. With this new formulation, the aim is to introduce a generic DSS for environmental conscious suly chain networ design. In addition to the basic logistics costs (raw material cost, fixed and variable facilities costs, and transortation cost), decision maers have to add a new cost due to the greenhouse gas emission. CO 2 emission can be caused by different suly chain activities. In this model, we basically focus on studying the imact of transortation activities on green suly chain erformance. But, it is easy to extend the same methodology in order to include sourcing, manufacturing and reverse logistics activities. The introduced model for environmental conscious suly chain networ design is generic and can be alied to different manufacturing contexts. Fundamental to the model is the use of mixed integer linear rogramming (MILP) technique to cature the interaction between the suly chain structure and its environmental imact. The Bill Of Material (BOM) and the suly chain structure considered are resented resectively in Figure 3 and Figure 4. { { { Figure 3. Bill Of Material (BOM) Figure 4. Suly chain structure Sets and indices In this study, the following sets and indices are used: P R P M P C M N D N Set of all roducts Set of raw materials Set of manufactured roducts Set of finished roducts Set of all nodes Set of customer zones S N Set of all subcontractors S S Set of subcontractors of roduct M V N Set of suliers of raw materials V r V Set of suliers of raw material r R Suc() Set of immediate successors of roduct P / C in the BOM S(Suc()) Function that returns all subcontractors for the set of immediate successors of roduct P / C M i Set of roducts that can be manufactured by subcontractor i S R i Set of raw materials that can be sulied by sulier i V K Set of all transortation modes K It is imortant to notice that we use the term sulier for raw materials roviders. Subcontractor is used for entities that mae manufactured roducts. Proceedings of the Fourteenth Americas Conference on Information Systems, Toronto, ON, Canada August 14 th -17 th

6 An integrated logistics model for environmental conscious suly chain networ design Parameters The strategic mathematical model requires the following cost arameters: λ i Fixed cost associated with the use of site i S V a i The start-u cost associated with the assignment of roduct M R to site i S V c Unit cost of roduct M R i S V i at site Unit transortation cost of roduct P from node i V t S to node j S Suc( )) D transortation mode K l Cost of a single shiment between nodes i V S and j S D using transortation mode K δ Cost er ton of GHG emissions The following data are also needed: α ' ( using Greenhouse gases emission factor er weight unit and er distance unit due to the use of transortation mode K g Number of roducts P / C required to manufacture one unit of roduct ' Suc( ) m Maximum number of sites that can be oened for roduct M R b i Caacity of node i S V for roduct M R te i Processing time on roduct M at node i S d d Number of roduct C required by demand node d D ρ i Lower bound (in %) on the aggregated caacity to be used if subcontractor or sulier i S V is chosen T i Total time available at the assembly line of subcontractor i S τ Maximum number of transortation modes that can be used between nodes i V S and j S D κ Volume caacity of transortation mode K ψ Weight caacity of transortation mode K π Weight of roduct P δ Volume of roduct P d( i, j ) Distance between nodes i V S and j S D Decision variables To find the otimal configuration of the networ, the following decision variables are required: F Number of units of roduct P shied from node i V S to node j S Suc( )) D transortation mode K X i Number of units of roduct M R manufactured or sulied by node i S V Y Binary variable equals 1 if roduct M R i ( using is assigned to node i V S and 0 otherwise Proceedings of the Fourteenth Americas Conference on Information Systems, Toronto, ON, Canada August 14 th -17 th

7 An integrated logistics model for environmental conscious suly chain networ design A Binary variable equals 1 if node i V S i is oen and oerational for at least one roduct and 0 otherwise U Number of shiments between nodes i V S and j S D using transortation mode K Z Binary variable equals 1 if transortation mode K is used between nodes i V S and j S D and 0 otherwise Mathematical formulation The objective function The total cost of the suly chain includes fixed and variable costs. Fixed costs are: fixed costs for facilities; assignment of raw materials to suliers; assignment of manufactured roducts to subcontractors. Variable costs are of five tyes: suly of raw materials; suly of manufactured roducts; shiment costs (related to the number of shiments); transortation costs; GHG emissions cost due to transortation activities. Therefore, the objective function to be minimized is given by: MinZ = λ A + a Y + c X + l U + i i i i i i i V S M R i S V M R i S V i S V j S D K t F + δ α π d( i, j) F M R i S V j S ( Suc( P)) D K M R i S V j S ( Suc( P)) D K (1) Model constraints For the MILP suly chain networ design model, there are many constraints to be considered. These constraints are of many inds including the balance constraints of all roducts, the caacity limit constraints, the minimum caacity occuation constraints, and the demand satisfaction constraint. The BOM constraints are imlicitly taen into account in the balance constraints. These elements are discussed below. For each raw material and for each manufactured roduct, the number of oerational sites should not exceed the maximum number allowed of suliers and subcontractors: i S V Y m ( R M ) i If a roduct (manufactured roduct or raw material) is assigned to a node, then the number of roducts sulied by this node must not exceed its caacity for this roduct: X b Y 0 ( R M, i S V ) (3) i i i If a subcontractor is chosen for at least one roduct, then the overall rocessing time used must not exceed the total available time at its assembly line or manufacturing facility: M i X te T A 0, i S i i i i There is usually a minimum amount of the aggregate caacity of a subcontractor that should be consumed to justify the establishment of a contract. This consideration leads to constraints (5) where the first term is the total time used at the assembly line or manufacturing facility of subcontractor i in order to manufacture all the roducts. The second term of the left hand side of the inequality is the minimum time to be used if subcontractor i is chosen: (2) (4) Proceedings of the Fourteenth Americas Conference on Information Systems, Toronto, ON, Canada August 14 th -17 th

8 An integrated logistics model for environmental conscious suly chain networ design M i X te ρ T A 0, i S i i i i i (5) To mae a deal with a sulier, the minimum caacity can also be considered. Here, the minimum caacity to be used is a ercentage of the total weight of all maximum quantities of raw materials that can be sulied by the sulier: Ri X i ρi bi Ai 0 ( i V ) Ri (6) The constraints of flow out of subcontractors / suliers nodes are given by the inequalities below: (7) X F 0 ( P, i V S ) i j S ( Suc( )) D K For each roduct, the quantity that arrives to a node must equal the quantity needed to manufacture next higher assemblies: F g X = 0 ( M R, i S( Suc( ))) ji j V S K ' Suc( ) ' i ' The following are logical constraints. A site is oerational if it is oen for one roduct at least: Y A 0 ( i S V, M R ) (9) i i i i The quantity of finished roducts shied from all its subcontractors to the demand node must equal the demand of that roduct: i S K F = d ( C, d D) id d For each coule of nodes, there is a maximum number of transortation modes that can be used. That leads to the following constraint: Z τ ( i V S, j S D) (11) K The quantity of roducts shied between two nodes is limited by the caacity of transortation mode and the number of shiments. While the first set of constraints (12) exresses the volume caacity and the second set (13) exresses the weight caacity: Ri Mi Ri Mi δ F κ U 0 ( i V S, j S D, K) π F ψ U 0 ( i V S, j S D, K) The number of shiments between two nodes for a given transortation mode is not nil only if the transortation mode is actually used. This yields to the following constraint: U MZ 0 ( i V S, j S D, K), M is a big number (14) A transortation mode is used between two nodes only if the number of shiments is not nil: Z U ( i V S, j S D, K) (15) The transort variables and the quantities sulied by sites are non negative: (8) (10) (12) (13) Proceedings of the Fourteenth Americas Conference on Information Systems, Toronto, ON, Canada August 14 th -17 th

9 An integrated logistics model for environmental conscious suly chain networ design F 0 ( R M, i V S, j S( suc( )) D, K) (16) X 0 ( (, i) R V M S ) (17) i Binary variables: { } Y 0,1, (, i) R V M S (18) i i { 0,1 }, { } A i S V (19) Z 0,1 ( i V S, j S D, K ) (20) The number of shiments must be integer: U integer ( P, i V S, j S( Suc( )) D, K ) (21) AN ILLUSTRATIVE EXAMPLE In this section, we show the exerimentation with an illustrative examle comosed of one finished roduct given its BOM. When solving the roblem, we assumed that the locations of all subcontractors and suliers are nown and that transortation costs include taxes and duties. Three freight transortation modes are considered: rail, air, and road. Rail and air transortation costs are assumed to include intermodal consideration, in the sense that they tae into account the costs related to the use of other transortation modes for shiing between the rail stations or the airorts and the different sites. The BOM contains a certain number of levels and comonents. It is illustrated in Figure 5. We assumed that all arts at the lowest level (RM001, RM002, RM003 and RM004) are raw materials. In this simle examle, there are only two semi finished roducts (SF001 and SF002). All the arts have two otential subcontractors or suliers as shown in Figure 5. The aggregated demand during the lanning horizon is 4,900 units of finished roduct FP001. FP001 [S1, S2] 2 1 SF001 [S3, S4] SF002 [S3, S4] RM001 RM002 RM001 RM003 RM004 [V1, V2] [V2, V3] [V1, V2] [V3,V4] [V1, V4] Figure 5. Bill of materials: Examle GHG emissions are limited to carbon dioxide (CO 2 ) caused by transortation activities. The quantity of CO 2 is calculated using emission factors for the three freight transortation modes considered in this examle and detailed in Table 1. An emission factor can be defined as the average emission rate of a given ollutant for a given source, relative to units of activity. Emission factors can be used to derive estimates of GHG emissions based on the amount of fuel combusted or on industrial roduction levels. The level of recision of the resulting estimates deends significantly on the activity in question. Different studies on how to calculate CO 2 emission factors for transortation activities are available. Emission factors considered in this examle are based on the recent study ublished in (Facanha and Horvath, 2007). The authors quantify emission factors associated with road, rail, and air transortation. Due to the comlexity of such assessment, some basic assumtions related to equiment caacity, equiment utilization, emty miles and fuel used were considered. Proceedings of the Fourteenth Americas Conference on Information Systems, Toronto, ON, Canada August 14 th -17 th

10 An integrated logistics model for environmental conscious suly chain networ design Transortation mode Tye Payload (tons) CO 2 (grams/ton-mile) Road Class 8b rail Intermodal rail 2, air Boeing ,385 Table 1. Freight transortation emission factors (grams/ton-mile) Table 2 summarizes the MILP model characteristics obtained for this simle examle. Number of variables Binary variables Integer variables Continuous variables Number of constraints Inequality constraints MILP statistics Table 2. MILP model characteristics Equality constraints The MILP roblem is solved by CPLEX Interactive Otimizer 10.0 in 0.03 seconds. For this examle, the otimal cost is 984,455 $. To observe the sensitivity of the total suly chain cost, we analyzed different scenarios where the quantity of CO 2 emission is reduced by a ste of 5%. Table 3 summarizes the different results obtained for the set of data used in the model. It shows articularly that the suly chain cannot go further a reduction of 17% comared to the base case. Percentage of GHG reduction GHG emissions (in g) Total Cost ($) Percentage of cost increase Base scenario 0% $ % Scenario 2 5% $ % Scenario 3 10% $ % Scenario 4 15% $ % Scenario 5 16% $ % Scenario 6 17% $ % Scenario 7 18% infeasible Table 3. Result for different scenario of GHG emissions reduction The results show also that the ercentage of cost change increases exonentially with otential GHG emissions reduction (Figure 6). Targeting higher ercentages of GHG emissions reduction will injure the total suly chain cost. From a managerial ersective, considering GHG reduction suggests that comanies within the suly chain networ should loo for new alternatives in order to absorb the additional logistics cost. 12,00% Percentage of cost increase 10,00% 8,00% 6,00% 4,00% 2,00% 0,00% 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% Percentage of GHG reduction Figure 6. Percentage cost increase as function of GHG emissions reduction Regarding decisions of transortation modes selection, we observe that lowest-cost s transortation modes are selected for the base scenario. As soon as we increase the ercentage of GHG reduction, less ollutant transortation modes are selected. Proceedings of the Fourteenth Americas Conference on Information Systems, Toronto, ON, Canada August 14 th -17 th

11 An integrated logistics model for environmental conscious suly chain networ design CONCLUSION The roosed decision framewor for green suly chain networ design is the first model in nature that integrates carbon cost exlicitly in the model. The examle demonstrated the otential of introduction of green ractices in suly chain networ design. It may hel managers to analyze the imact of GHG emissions reduction on the suly chain configuration beyond the traditional financial aroach. In fact, they may quantify the cost to add if they decide to go beyond a green initiative. The alication of the model shows how the suly chain may balance carbon emission and total costs in a more effective way. Here, we tae only into account CO 2 emission caused by transortation activities, but the model can be easily extended to add rocurement; manufacturing and reverse logistics activities. The same aroach and methodology can be alied to different real suly chain cases and the evaluation of their current ositioning, in terms of total logistics cost and GHG emissions levels, may be assessed and comared thans to the efficiency Pareto-frontier curve (Figure 6). In this model the delivery lead times were not in concern. But, we believe that additional constraints related to that such as service level and delivery lead time to customers may influence the solution characteristics and the suly chain configuration. It is clear that green and environmental suly chain management has reached a level that requires a coherent and long term suly chain strategy. Regulations are coming soon and will affect all industries. There is going to be carbon legislation that uts rice on carbon and creates carbon marets. As such, suly chain decision maers should establish the GHG footrints of their oerations. 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