SUPPLY CHAIN NETWORK DESIGN CLASSIFICATIONS, PARADIGMS AND ANALYSES

SUPPLY CHAIN NETWORK DESIGN CLASSIFICATIONS, PARADIGMS AND ANALYSES Reza ZanjiraniFarahani 1*, ShabnamRezapour 2, Tammy Drezner 3, Samira Fallah 4 (1) Department of Management, Kingston Business School, Kingston University, UK (2) The School of Industrial Engineering, The University of Oklahoma, Norman, Oklahoma, USA (3) Steven G. Mihaylo College of Business and Economics, California State University-Fullerton, Fullerton, USA (4) Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran Abstract Designing a supply chain network (SCN) consists of determining the network structure and dealing with various decisions from strategic decisions on location and capacities of facilities to operational and tactical concerns about transportation or inventory management policies. The structure of a SCN has a great influence on its future performance and costs and therefore, network design is a critical concern in supply chain management (SCM). Traditionally, most studies in the area of supply chain network design (SCND) mainly focus on monetary objectives. They seek designing a SCN with the aim of minimizing total costs of network or maximizing its overall profit. Nevertheless, recently various new paradigms have emerged in SCM field. In order to guarantee profitability and maintaining in today's market, a SCN not only has to minimize the overall costs but also should be flexible and responsive to the environmental changes while considering different concerns such as environmental and social factors rather than economical ones. As a result, designing a profitable SCN requires a broader view which takes such primary concerns in to account. In this paper, this concern is emphasized and different paradigms in SCM with particular focus on network design are reviewed and some current studies in each paradigm are addressed. In addition, we propose a framework of major studies in SCND area and we summarize the strategic decisions which are answered in each section. Keywords: Supply chain management; Network design; Paradigms; Objective Functions; Classification. * Corresponding. Fax: +44 020 8417 7026; Tel: +44 020 8517 5098; Email: zanjiranireza@gmail.com 1

1. Lean Supply Chain Network Design (SCND) Lean is a philosophy that originally was created for production systems. Lean production focuses on elimination of any waste, process or resources that have no value-added for the end customer. 1.1. What is lean Supply Chain (SC)? SC is primarily developed for lean production with the aim of achieving cost reduction by eliminating non-value adding activities, so the objective function of these models is to minimize total cost of the SC. Shen and Qi (2007) developed a model for optimizing the total cost of a SC when the retailers are uniformly distributed. The result was a nonlinear integer programming model which is solved using Lagrangian relaxation. 1.2. What is lean SCND? Shen (2007) presented a recent review of lean SCND literature. He classified the models based on their objective functions (minimizing chain s total cost or maximizing its profit) and investigated different operational costs. For studying these models, Shen (2007) introduced the model presented by Daskin et al. (2002) as the basic model. All other models are extensions of Daskin et al. (2002) with new assumptions. The overall characteristics of this basic model are summarized in Table 1. Table 1. The main characteristics of lean SCND basic model (Daskin et al. 2002). - One or more supplier, distribution centers (DCs) and retailers exist - The location of supplier and each retailer is given Major Assumptions - The demand quantity of any retailer is uncertain - All lead times of DCs are equal - Inventory holding costs are equal in all retailers Number of Single product Products Number of Periods Single period Network Flow Forward Capacity of Infinite Facilities Outputs of the model Objective Function - Finding the optimal number of DCs - Finding the optimal location for each DC - Assigning the retailers to DCs - Determining the optimum ordering policy and the amount of safety stock in DCs Minimizing the overall cost consisting: - Fixed cost of locating the facilities - Cost of shipping products from DCs to retailers - Total cost of working inventory - Cost of safety stocks 2

The basic problem is formulated as a non-linear integer-programming model. Various approaches were proposed in different papers to solve the model. Daskin et al. (2002) proposed a Lagrangian relaxation solution algorithm and a number of heuristics for finding the feasible solution. Shen et al. (2003) reformulated the problem as a linear integer set-covering problem and applied column generation for finding the solution. Shu et al. (2005) also presented an algorithm based on column generation and variable fixing to solve the basic model.shen (2005) developed the general form of the basic model. He designed a multi commodity network model for a SC. To solve the model, he applied Lagrangian relaxation embedded in a branch and bound algorithm. Shen and Daskin (2005) extended the basic model by considering customer service issues. They developed a multi-objective model which explores the trade-off between cost and customer service level. They proposed two solution approaches (a weighting method and a heuristics based on genetic algorithm) for optimizing the model. Ozsen et al. (2006) developed a model based on the basic one which considers a limited capacity for each DC. They proposed a Lagrangian relaxation solution algorithm for solving the model. Shen (2006) proposed a model for designing supply chain network (SCN) to maximize the SC profit. While cost based approach tends to satisfy the overall demand, it is not the case when the objective is to maximize SC profit. This model is based on the fact that even in a monopoly; it is not always beneficial to satisfy all customer demand. In some cases the cost of this approach may exceed the profit. Their proposed model allows a firm to choose whether to fulfill an order or not. The problem is formulated as a set-covering model and they use branch and price algorithm to solve it. He also generalized the model under random demand and limited capacity assumptions. Different cost components are considered in SCND problems with lean consideration. While most models consider the associated cost of strategic decisions such as location and capacity improvement, there are some with focus on operational costs of the SC. Due to the major impact of SCND on operational decisions, consideration of the associated costs of this level is necessary. Often it is difficult or even impossible to determine or predict all operational costs. As a result, most models only consider the important ones such as inventory holding, ordering, distribution, routing, and operating costs. 1.3. Observations of Lean SCND literature In the majority of the literature in the field of lean SCND, the objective function is to minimize the total cost of the chain. In these papers, fixed income for the chain is assumed. There are two fundamental and restrictive assumptions in such papers: The markets in which the chain will supply its product are exclusive and there are no competitors in the markets offering the same or substitutable products. In today's markets, this 3

assumption is far from reality and its application is restrictive. By globalization and communication technology improvements, there are few exclusive markets. For new fiercely competitive markets, we need new SCND models. It is assumed that the chain will have to supply all market s demand. This assumption is not applicable for private (non-governmental) SCs. There are only few studies of SCND with lean consideration in order to impose the new requirements of today's markets (mentioned in the first assumption). Hence, revising the existing SCND models to consider the new requirements of today's markets is one important gap of lean SCND literature. SCND models consider a tradeoff between the income and the cost of a chain. Ignoring the existing competitors leads to imprecise modeling of SC s income and this can result in a significant mistake in designing the network structure which is a strategic decision. In some recent papers such as Shen (2006), the second assumption is revised and there is no such restriction on the chain. Instead of cost minimization, these models maximize chain s profit in the exclusive markets. Therefore, one of the variables of these models is the quantity of the product supplied to each market. There are many different competitive factors in competitive markets. Some of them (such as distance from customers, quality of retailers, capacity of retailers and so on) are determined in the SCND stage which are called strategic competitive factors. There are some other competitive factors such as retail price and service level which are operational decisions affected by the SCND decision. Thus, prediction of the income for a chain with unknown network structure is impossible and can lead to significant deviation from its real amount. This issue determines the importance of considering the competitive lean SCND problems. 2. Agile / Responsive SCND 2.1. What is agility / responsiveness? Today, the needs and expectations of customers are rapidly changing. In such a changing, competitive environment companies require new strategies and technologies to improve their responsiveness and increase their flexibility in a cost effective way. Therefore, they focus on developing agile SCs to reduce time to market of their products while achieving minimum total cost. Christopher and Towill (2001) addressed the major differences between lean and responsiveness paradigms and showed how these two could be combined to create competitive SCs. 2.2. What is agile/ responsive SC? Being highly flexible is a must for the responsive SCs (RSC) in order to respond to environmental 4

changes by quick reconfiguration. Hence, there is a need for integrating the concept of agile manufacturing (AM) and Supply chain Management (SCM) to achieve agility in a SC environment. According to Gunasekaran et al. (2008), a responsive and flexible SC is achieved through the interaction of three major elements i.e. a) value chain or a collaborative network of partners, b) information technology (IT) and c) systems, and knowledge management (Figure 1). A level of compatibility and interactivity is essential for a RSC system to make it possible for a firm to cope with the changes and increasing the complexity of organizations and markets, especially in a networked economy. Information technology and system Network of partnering firms Responsive SC Knowledge management Speed & flexibility Figure 1.Major enablers of RSC (Gunasekaran et al. 2008). Christopher and Towill (2001) developed a three level framework to introduce a RSC. The first level represents the major principle of a RSC in terms of rapid replenishment and postponement fulfillment. Level two constitutes the individual programs that must be applied to support the principles in the first level, among them are quick response, the agility of organization and lean production. The third level identifies the actions that must be taken to enable programs represented in the second level, such as eliminating waste, comprising the time and enriching the information. Christopher (2000) suggested that in time-based competition era, time is a competitive weapon and those SCs which have the capability to shorten delivery times and be more responsive to their customers will survive. He addressed the concept of responsiveness as a key towards competitiveness and survival. Prater et al. (2001) suggested that strategic SC responsiveness is crucial and vital for every firm to evaluate the trade-off between SC responsiveness and their own vulnerability. They define the concept of supply chain exposure which means to what extent an agile SC is vulnerable and indeed requires restructuring and adjustment. They concluded that the number of geographical areas which a SC covers and the number and speed of transportation modes are among the factors 5

affecting SCs exposure as well as environmental, political and technical issues. Dotoli et al. (2006) presented a single and multi-objective optimization model to design the structure of an integrated e-scn. They considered an internet-based distributed manufacturing system that has several stages such as supply of raw material, intermediate supply, and manufacturing, distributing, retailing and recycling. These stages are related by means of material and information flows. The objective of the model is to minimize costs by considering some constraints such as bill of material (BOM), mutual exclusion and path. The developed model is applied to two case studies in the desktop computer production industry. Agarwal et al. (2007) derived the variables influencing the responsiveness of SC and their interrelationship by using interpretive structural modeling (ISM). By studying a case SC and considering the expert judgment, they concluded that improving quality, satisfying customers, introducing new products, improving service level, minimizing costs, delivery speed and reducing lead time are the primary variables which impact the responsiveness of a SC. Li et al. (2008) provided a framework to investigate the relations between responsiveness of SC and competitiveness of firms. By taking a work design perspective to investigate levels of SC responsiveness, they suggested three levels as follows:i) an episodic design level, ii) operational design level, and iii) strategic design level. The episodic design is the result of integrating alertness of a SC to changes in tasks to its capability for responding to them by applying existing resources. Operational design responsiveness is concerned with integrating the alertness of SC to changes in demand or supply with its capability to respond to them by adjusting existing type of work episodes. The strategic design responsiveness concerns with SC s alertness to internal and environmental changes and its capability to respond to these changes by reshaping or restructuring current operational systems. You and Grossmann (2008) considered lead time as a responsiveness criterion and net present value as an economic criterion to address the problem of designing and planning SC when demand is uncertain. They formulated the problem as a bi-criterion optimization to minimize expected leadtimes and to maximize the net present value. They applied the ε-constraint method to produce a Pareto-optimal curve. The curve revealed the change in optimal net present value, structure of SCN and level of safety stock in different lead time values. Baker (2008) investigated 9 case studies to understand how distribution centers are designed and operated in each individual business units for rapid responding to the markets. By means of the solution each under studied company uses to deal with proposed problem, a framework is formed to address the problem and also form a basis for future research of agility application in distribution center level. Luo et al. (2009) developed a model to overcome the difficulty of processing the information of 6

a large number of potential suppliers. Their model was based on Radial Basis Function Artificial Neural Network (RBF-ANN) and makes it possible to assess potential suppliers with respect to different quantitative and qualitative criteria. Pishvaee and Rabbani (2011) studied the responsive, multi-stage SCND problem and proposed two mixed integer programming (MIP) models for two conditions: (a) to allow direct shipment and (b) to prohibit direct shipment. They studied the structure of SCND problems by means of graph theoretic approach; in this way they eliminated the complexity of the MIP models. The ability of modeling SCND problems by a bipartite graph was also proved. Finally, by means of graph theoretic view to the structure of problem understudied, they developed a novel heuristic solution method. Pan and Nagi (2013) discussed the concept of virtual organization as an efficient tool for agile manufacturing. The virtual organization is formed by agile rivals who share their resources as well as their core competencies to accomplish a product. The accomplishment of this particular product is not feasible without this integration. They also addressed the relevance and importance of considering strategic and tactical planning issues in agile manufacturing. Pan and Nagi (2013) consider the problem of forming the virtual organization to form a particular product in a multi echelon and multi period supply chain. They formulate this problem as a mixed integer linear model with the aim of selecting the firms to form the organization as well as addressing the tactical decisions of each company such as production, inventory, and transportation. The objective function of the model minimizes the total costs including production, holding raw material and finished products, transportation costs and capacity as well as alliance associated costs between two companies. They solved the proposed model by a Lagrangian solution heuristic. Liu and Papageorgiou (2013) addressed the problem of planning and optimizing the production, distribution and capacity of a global supply chain of an agrochemical company. In this problem, the initial capacity of the firm could not satisfy the growing demand and therefore applying a capacity expansion strategy was required. They formulated the problem with a multi-objective mixed-integer linear programming (MILP) model which seeks minimizing the total cost, flow time and lost sales. In other words, they considered cost (cost of raw materials, inventory, transportation, formulation and duties), responsiveness (total flow time) and customer service level (total lost sales) simultaneously. They applied ε-constraint method as well as lexicographic mini-max method to solve the problem and determine the optimal capacity, production and flows as well as level of inventory and sales. 2.3. Observations of Agile/ responsive SCND literature Competitive business environment makes the chains to be not only responsive but also lean. The 7

results of this responsiveness in the attractiveness of the chain for the customers and the chain's future income have not been investigated yet. It is frequently mentioned that only responsive chains can be successful in today's competitive markets, but there is no research considering responsiveness as a competition factor in the markets. In other words,impact of responsiveness to the income of the competitors should be analyzed. Being responsive is costly and the trade-off between responsiveness and its income should be investigated as future research for SCND.The reason for this gap is the difficulty of predicting this income.there are some other factors that can be considered such as the index of responsiveness in the competition of the competitors which have been neglected in the literature. Examples include lead-time required for supplying the ordered goods or ability of the chain to respond to all the potential demand by keeping extra capacity (or holding extra inventory). Naturally, customers preference determines which indices to select. 3. Green, reverse and closed-loop SCND 3.1. What is greenness? Traditionally, separate units in organizations were responsible for ensuring environmental excellence in different steps of firms value chain. But nowadays, after the revolutions in quality concerns (1980s) and also revolution in SCs (1990s), it became clear that in order to have the best results in considering environmental issues, integration between different ongoing operations is needed (Srivastava2007). One of the primary concerns of industrialized countries has become reducing wastes. As the result, in SC literature, some works are focused on physical design of product recovery networks which also called backward or reverse logistics (RL). The major objective in this type of studies is to identify the characteristics of these networks; comprising physical locations, facilities, and transportation links, communicating and transmitting used products from former users and re-using them. Recovering products occur in three major steps; firstly used products are collecting from disposer market to recovery facilities, this step is called collection phase. At last, recovered products are collecting from recovery facilities to the demand points of the re-use market. This phase is called re-distribution step. The specific characteristics of product recovery determine the structure of intermediate network. This network could consist of a simple processing in a single facility or multi complex processing steps. Green consideration in SC includes any attempt in the design and operating stages of a SC to reduce the environmental effects of its activities. 3.2. What is reverse and closed-loop SC? Closed-loop SC.It is possible that disposer market and re-use market coincide (Fleischmann et al. 2002); this case is called closed-loop SC,which is more complex to design due to the interaction 8

among collection and re-distribution. Forward and reverse SC. Reverse SC flow, which considers flow of the waste after the end consumer emphasizing processes like collection, recovery, recycling, etc, are becoming important. While most traditional models only consider forward flow, some studies consider reverse flows and a few consider both flows.the reverse and forward network models have some major differences. One of the primary differences is the activities which carried out within network. In reverse networks, the span is considered from a market which sets free used products (disposer market) to the other one which demands recovered products (re-user market). As it is usually difficult to forecast the demand for recovered products, uncertainty is a common characteristic of reverse networks (Fleischmann et al. 2002). While in forward networks it is possible to control the quantity, quality and timing of inputs. 3.3. What is green SCND? After emerging of Green SC management (GrSCM) concept, green SCND concerning environmental considerationsgained a growing interest. The main focus of GrSCM is on decreasing the deterioration of environmental impacts while gaining higher business profits (Wilkerson 2005). GrSCM is defined as integrating environmental thinking into SCM, including product design, material sourcing and selection, manufacturing processes, delivery of the final product to the consumers as well as end-of-life management of the product after its useful life (Fleischmann et al. 2002). Pishvaee et al. (2010) presented a comprehensive review on green, reverse and closed-loop SCND. Most of the research publications in the literature focus on forward SC flows which is material flow from upstream toward downstream and end consumers. 3.4. Green SCND literature Meixell and Gargeva (2005) reviewed and classified the works with focus on decision support models for designing the network structure of global SCs in SCND literature.they investigated the related models from four different dimensions: i) decisions made in this kind of problems,ii) metrics introduced and used to evaluate the performance of global chains,iii) integration degree in the network design decision making process and iv) how globalization issues considered in the formulated models. They compared the research works in this field and the real needs and challenges of real global SCs and concluded that there is not any appropriate correspondence between them. Finally they proposed some future research to motivate more practically-oriented research in this field. Literature of Green SC is very extensivecompared to agile SC. Srivastava (2007) categorizes its literature into three main groups based on its context in SC: 9

Research on the importance of green considerations in SC Research on green design: This kind of works usually deals with lifecycle assessment of the product or process. How a product should be designed or a process should work to be more environmentally friendly? Research on green operations: These problems deal with operational aspects of reverse logistics, open-loop or closed-loop SCs and their network design problem. Based on the classification suggested by this paper, we only focus on the third group. In the field of SCND, mainly three kinds of works have been done in the proposed third group. SCND problems of mainly forward chains with the objective functions of waste management (reduction in pollution, disposal, etc.): First we review some of the recent works in the third category, when the objective of the SCND models is minimizing the wastes or pollutions. Krikke et al. (2003) developed a quantitative model to support decisions on product and logistics network design. The model was applied to the problem of SCND for refrigerators. They considered cost as a linear function of the volume with a fixed set-up component for facilities while environmental issues were considered as linear functions of energy and waste. Hugo and Pistikopoulos (2005) developed a multi-objective methodology based on operations research (OR) to explicitly include life cycle assessment (LCA) in process of SCND. Their model includes strategic (such as site selection, demand allocation and etc.) as well as operational decisions (including optimal production profiles material flows within the SC). Hugo et al. (2005) proposed a multi-objective optimization approach for hydrogen networks, with the aim of investigating the trade-offs between investment and greenhouse gas emissions. Bojarski et al. (2009) addressed the problem of optimizing the design and planning of SCs when environmental issues are considered. Strategic decisions considered in this model consist of locating facilities, selecting processing technology and planning production-distribution. To perform the assessment of impacts the IMPACT 2002+ methodology was selected and a feasible implementation of a combined midpoint endpoint evaluation was provided. Guillen-Gosalbez and Grossmann (2009) considered the Eco-indicator 99 code (for quantifying the environmental impact) and formulate a bi-objective stochastic mixed-integer nonlinear program (MINLP) to maximize net present value and minimize environmental impacts in a model. Pinto-Varele et al. (2011) also exploit Eco-indicator 99 (particularly, damages to health of human caused by electricity and diesel consumption over the entire SC) and addressed the problem of SCND when the aim is to maximize the annual profits with consideration of environmental issues. They focused on minimization of carbon footprint and used symmetric fuzzy linear programming (SFLP).Pishvaee and Razmi (2012) presented a multi-objective fuzzy model in order to design an environmental SC. The model minimizes the multiple environmental impacts and the total 10

costs as well. In order to asses and quantify the environmental impacts of alternative SCN structure, a life cycle assessment-based (LCA-based) method is applied. Moreover, an interactive fuzzy solution approach is used to solve the proposed model. Wang et al. (2011) proposed a multi-objective optimization model to address the environmental investment decisions which make a tradeoff between total cost and the environment influence. CO2 emission considered as the only environmental index in this model. SCND problems of backward flows in reverse logistics or closed / open loop SCs:There are other green SCND problems that consider environmental considerations from the perspective of designing the backward network of recollecting, reassembling, remanufacturing, etc. process of used product. Now,we briefly review some of the recent works in this field.listes and Dekker (2005) developed a stochastic programming based approach to address the problem of designing a two-level network to locate storage and cleaning facilities across a set of potential sites while the net revenue is maximized. The model is applied to a real case on recycling sand from demolition waste in Netherlands. Lee and Dong (2008) presented a deterministic programming model to address the problem of designing a logistics network for recovery of end-of-lease computer products. In order to reduce the complexity, they proposed a heuristic approach with two steps which decomposes the primary problem in to a location-allocation and a revised network flow problem. Min et al. (2006) developed a nonlinear mixed-integer programming model as well as a genetic algorithm to address the problem of reverse logistics with product returns. Their main focus is to determine the number of centralized return centers and their location to collect, sort and consolidate return products which have large distance to repair facilities. Srivastava (2008) presented a multi-product, multi-echelon and value recovery network model to maximize reverse logistics profits. They devised a strategy to reduce the size and complexity of the problem. They followed a hierarchical optimization approach with two steps. In the first step, their model finds the location of collection centers by considering strategic and customer constraints criteria and in the second step, the model determines the location and capacity addition decisions for rework sites at different time periods. Beamon and Fernandes (2004) presented a multi-period integer programming model which uses the present value method to address the problem of designing product recovery networks. The main purpose of the model is to determine warehouses and collection centers to be open and their sorting capabilities as well as the amount of material flow between them. Francas and Minner (2009) discussed the network design problem for a company which manufactures new products and also remanufactures the used one by replacing some components or reprocessing them. Under uncertain demand and return flow, they developed two different network configurations and 11

evaluate the capacity and performance of each structure. In the first structure, the new and returned products are considered to be processed by share facilities while in the second ones separate facilities carry out the manufacturing the new products and remanufacturing the used ones. Moreover, they considered two market types; firstly, they assumed that manufacturing and remanufacturing products are perfect substitutes and together satisfy the market demand. Secondly, they consider a market in which the customers are not indifferent to the new and reproduced products and each has its own demand. They formulated the problem as a singleperiod, two-stage stochastic model and discussed some numerical studies. The results show the decentralized network is more beneficial for multi-product manufacturing and especially in separate markets for used and new products while in case of common market, the integrated one is much more profitable. Francas and Minner (2009) also state that the size of network, costs of investment on manufacturing capacity as well as the structure of markets have significant impact on the choice of a network configuration. Combination of the above cases:additionally, there are few works that consider both of the aforementioned issues simultaneously such as Feng and Gerals (2008). They developed a biobjective optimization model to address the problem of designing a reverse logistics network with returns, in which the returns require repair services. The objectives of the model are to minimize the total costs and also to minimize the total cycle time tardiness while determining the arrangement of facilities capacity as well as the associated transportation flows between customers and the facilities. They applied a combination of scatter search, the dual simplex method and the constraint method to solve the model. Qiang et al. (2013) addressed the problem of designing a closed-loop supply chain network which consists of suppliers of raw materials, manufacturers and retailers. They modeled the problem from the associated entities point of views to derive their optimum conditions. From the retailer view point, they assumed that each retailer is responsible to satisfy the uncertain demands of customers by new products or remanufactured ones in its own demand market while the customers are indifferent to either product. They also assumed that the recycled products are collected directly from demand market by the manufacturers. They formulated the problem as a finite-dimensional variational inequality problem. It is usually mentioned in the literature of green SC that legislation of countries especially in Europe and East Asia makes the industries manage their own products when they have reached their end of life (EOL). However recently a great number of manufacturers became aware of the profit opportunities of remanufacturing and start product collection and recovery voluntarily. Carpet (Fishbein 2000), packaging (Rondinelli et al. 1997), tires (Ferrer and Whybark 2003), automotive 12

parts (Hormozi 1997), batteries (McMichael and Hendrickson 1998), photocopiers, cellular telephones and high-tech manufacturers (Ayres et al. 1997) are some of these instances. In addition to profit opportunity, product take back by manufacturers provides product durability and brand reputation which is important in today's competitive markets. Toffel (2004) believes that product remanufacturing reduces production cost of industries, improves their brand reputations, helps the manufacturers to easily adapt with new emergent customer expectations and protects the industries aftermarkets. All these factors can improve the competitive strength of a company in associated markets in order to capture more demand. Some companies have found out that refurbished components, resulted from recovered take back products, can be substituted instead of new parts as spares or in remanufacturing process. For example Mercedes-Benz Company recollects, disassembles, refurbishes the important component of EOL cars and sell them to its customers with significant discount. Similarly in 1999, Ford Company started to buy EOL cars in different countries to have another spare parts resource. Another example is Xerox Company. This company saves millions of dollars yearly by recovering its EOL photocopiers. As it can be seen, remanufacturing can generate new markets and income resources for the companies. Sometimes, a new kind of competition can emerge among the new and recovered products of companies which can also affect the income of the chain in another way. It is obvious that remanufacturing strategy, in addition to its environmental effects, can affect the future income of SCs. Using refurbished components of EOL products can significantly reduce the production costs. For example Xerox could reduce 20-70 percent of its consumed materials, water and energy by using refurbished components of its EOL products. Reducing production costs can lead to lower prices. Lower price is one the most important factor of competition. On the other hand, activating product remanufacturing in an industry can improve the environmental image of its brand. A survey done by King and Mackinnon (2002) demonstrates that environmental efforts of a company can strongly influence the customers intention to purchase the products of that company. For example when Kodak, Fujifilm and Hewlett-Packard started to take back single-use cameras and EOL computers respectively, they received positive media coverage which influenced their market share. Therefore, it seems necessary to investigate the effects of using recovery strategies in brand reputation, captured market share and future income of the chain in competitive markets. Using these strategies can be considered as a competition factor of a chain. However there are no quantitative models in the literature that investigate the effects of these factors in the competition ability of the chain.recently, new expectations of customers make some companies shift to product recovery. For example there is a growing trend in the computer industry to lease rather than sell to customers. Following this strategy has created the necessity of product remanufacturing after 13

leasing expiration. For example, business customers in USA expect Dell Company to collect EOL computers as a service of selling new computers. Additionally, some governments force governmental agencies to only purchase from companies which recover their own products (Majumder and Groenevelt 2001). Hence, analyzing the impact of product recovery in customer behavior and capturing market share seems necessary. Protecting aftermarkets is the other advantage of product remanufacturing. Aftermarket is the market of parts and accessories which helps to maintain or improve previous purchases. These markets are so profitable for companies,because they can attract new customers by supplying likenew products with prices which are 45 to 65 percent lower than the new ones. Allowing independent small remanufacturers to enter this market can damage the brand reputation of the new product and is considered as a threat for the potential demand of these markets. Recent interest of some auto manufacturers such as Ford and Mercedes Benz in their EOL cars is for precluding independent competitors from approaching their branded spare parts. Some companies such as Lexmark offer discount for customers to encourage them to return their printer cartridges for remanufacturing (Toffel 2004). It is obvious that greenness of SCs has a significant impact in their attractiveness for customers and as the result theirmarket share and income. Remanufactured products help the companies to keep their aftermarket and provide a new income source for it. Using product recovery strategy improves the reputation of the companies brands and enhances their market shares. Producing and supplying remanufactured products can increase the number of competing products of the markets. In this case, analyzing the competition of the competing products and predicting their resulted income is necessary. The point is that greenness by itself can be considered as a competitive advantage for a SC; therefore, we can look at it as an opportunity (which persuades consumers to buy) rather than a costly function.making the chains green consists of several activities such as a) establishing collecting centers and operational activities of EOL product collecting; b) establishing refurbishing centers for checking EOL products, disassembling and segmenting valuable components; c) remanufacturing components; d) recycling materials; e) disposing the disposals. All these activities impose some fixed and operational costs for the chain. In the SCND stage, designers should try to impose an appropriate tradeoff between these costs and the predicted income. Obviously, the need for this kind of efforts in the literature of the green SCND is completely visible. 4. Sustainable SCND 4.1. What is sustainability? The World Commission on Environment and Development (WCED) defined sustainability as using 14

resources to meet present needs while the ability of future generations for meeting their needs is not compromised. The whole process of designing, sourcing, producing and distributing products consumes a huge volume of resources and as the SCs concern the product from processing raw materials to delivery to the customer, they can play a primary role in preserving environmental sustainability. Traditionally, sustainability refers to the ability of a business to maintain in the long term. But this concept has been improved in the 21 st century and now is referred to as the ability of an organization to meet the financial, social and environmental requirements of the present generation without disabling the future generations to meet their requirements (Brundtland 1987). This new definition not only considers financial performance, but also addresses environmental and social performances of the firm as three key issues of sustainability. Although these three aspects may look unrelated in the first glance, they are strongly interconnected and interdependent in the real world.enhancing the scarcity of resources and increasing the cost of energy and other resources are the main motivations for companies to move toward sustainability. Recently, companies have realized that considering sustainability as a strategy in all of their business activities can reduce risk, increase business value, be considered as a competitive advantage, and make tangible and intangible profits. 4.2. What is sustainable SC? The need for taking necessary actions towards increasing consumption of natural resources as well as increasing awareness of society towards environmental issues has changed the conventional view of SCs and emerged Sustainable SC (SSC) concept.therefore, sustainability has three main dimensions as economic, environmental and social (Buyukozkan and Berkol 2011) and SSC management is defined as managing the flow of material, information and capital along with cooperation between firms for achieving economic, environmental and social goals. Interested readers can refer to updated references on sustainable SC; here are several instances: Abbasi and Nilsson (2012), Gimenez and Tachizawa (2012), Carter and Easton (2011) and Browne et al. (2005). 4.3. Sustainable SCND literature Neto et al. (2008) addressed the problem of designing logistics networks in terms of environment and costs by developing a multi-objective programming model. They considered transportation, manufacturing, use of products, testing and end-of-use activities as the primary activities which have an impact on environmental and economic aspects of a logistics network. The model is also applied in a real case in the European Pulp and Paper sector. 15

Nagurney and Nagurney (2010) addressed the problem of determining the capacity of several SC activities of a firm such as manufacturing, storage and product distributing. The objective of their model is to minimize the total design/operation costs as well as to minimize the emission associated with manufacturing plants, facilities for storage and transportation modes.different other research has been done in the literature of sustainable SCND including Seuring and Muller(2008), Chaabane et al. (2012), Gupta et al. (2011) and Gunasekaran and Spalanzani (2012). Some people consider sustainability as a huge cost for a SC. In other words, some believe social and environmental objective functions have conflict with the main objective of the SC (making profit). We believe this is not necessarily true if we consider a long period of time.risk mitigation is one of the most important benefits of sustainability. Improving the sustainability of a company can reduce health, environmental, and business risks. For example consider the disaster caused by BP Deepwater Horizon Company in the Gulf of Mexico. The company concentrated on short term cost reduction strategy rather than long term sustainability. This inappropriate strategy led to loss of 11 lives (health risk), unrecoverable damage of ecosystems (environmental risk), highly damaged reputation and financial performance (business risk). The company had to spend $1 billion for cleaning up and lost $7.5 billion in its shareholder value (Laszlo 2003; Kahn 2010; Bluestein 2010). In the 21 st century major part of large customers require their suppliers to be sustainable (such as Wal-Mart), in the other case, suppliers face with the risk of lost business to sustainable competitors in the markets. The other key benefit of sustainability is process improvement. Companies choose sustainability as one of their main strategies to try and decrease resource and energy usage, enhance productivity, and reduce waste in their system. These eco-efficiency activities lead to cost reduction and more profitability of their organizations. Clif Bar Company is a healthy energy bar producer with $150 million revenue. This company saved $400,000 each year by eliminating shrink wrap from its packaging systems. An auto dealership in Florida could save $60.000 each year by implementing an energy efficiency improvement program. Reducing electricity consumption and natural gas emission are the other advantages of this program for this dealership. The third advantage of sustainability is leading firms to design and re-design environmentally friendly products which are made from natural and chemical free materials. For example, some restaurants try to add some organic food offerings for their customers. The other advantage of sustainability is opening up new markets for the company or emerging existing ones with new marketing models. For example, the income of ZipCar Company increased 670 percent between 2005 and 2008 by expanding its market through car rental offering. By investigating the real needs of untapped auto market segment, they conclude that this kind of people do not need a car but frequently need one to use for a short time. They recognize that these people 16

need transportation service not a car. Long term lease models are the other kind of these new marketing models. Other value source in this category is finding secondary markets for recovered products and by-products. The other category of increased business value due to enhanced sustainability is amended brand reputation, customer and employee loyalty, company culture and stakeholder perception. At first, StonyField Farm was a small business with few employees and some cows. But customer loyalty and word-of-mouth promotion transformed it to a worldwide producer of organic daily products in eighteen years with more than three hundred million dollars annual income. Employee loyalty is another critical issue for companies. Because the cost of hiring, training, and productivity losing of a new employee is about 2 times more than that position's annual salary. According to a study eighty percent of respondents to a survey in developed countries prefer to work in firms with reputation of sustainability reputation and this figure is higher in US (Widger 2007). The other benefit of sustainability is called changing the rules. Leading companies in sustainability can achieve a position which allows them to set industry standards. Such companies can benefit from this leadership position. It leaves its less sustainable competitors behind and solidifies the competitive advantages of the leading firm. Clearly, sustainability is a strong competitive advantage for companies and can significantly influence the income of the organizations. Therefore, modeling the consequences of sustainability in the competition ability of the firms and their future income is necessary in SCND stage. As said before, companies should consider sustainability as one of their core strategies. Since SCND is a strategic decision, it should be consistent with all other strategies of the company. Some of decisions related to sustainability such as selecting objective markets, product and production process redesign affect the network structure of the chain. According to the literature of sustainable SCND mentioned above, lack of models to quantify these effects of sustainability in the competition ability of the chain and its future income is obvious. Ignoring these effects and their consequences leads to a large deviation from optimal SCN structure and significant losses of the chain. 5. Risk management in SCND 5.1. What is risk? Risk is usually defined as the probability of an unpleasant event occurrence. Whereas some areas like project management consider both positive and negative risksoften negative aspects of risk are highlighted in the literature. Risk management is identification, assessment, prioritization, monitoring, and control of risk and uncertainty. Some believe that in order to manage risk, we need to consider phases like preparation, prevention, mitigation, etc. In addition, to address the definition of risk it is important to discuss its association with uncertainty. Rosenhead et al. (1972) distinct the 17

concept of risk and uncertainty by dividing the decision making environment to three main groups: certainty, uncertainty and risk. The uncertainty occurs in situations where it is not possible to attribute the probability of a decision s outcome. On the other hand, in a risky environment the probability distribution of a decision s outcome is known. However, besides probability distribution, there are some other ways (such as fuzzy sets and possibilities) to model the likelihood of a decision outcome (Klibi et al. 2010). As the result, the proposed distinction between risk and uncertainty is not accepted by all researchers. Uncertainty results to risk, due to the fact that risk is inherent in uncertain information, models and situations. Interested readers can refer to Baghalian et al. (2013) to see a comprehensive review about risk, uncertainty and robustness with focus on SC. 5.2. What is risk management in SC? Tomlin (2006) presented three different strategies to reduce the effects of uncertainties and disruptions in SCs asadapting inventory control system, multi-sourcing and also proactively mitigating risks. Chopra and Sodhi (2004) categorized the associated risks of SCs into eight groups: delay, forecast inaccuracies, procurement failures, disruptions, system breakdowns, intellectual property breaches, capacity, and inventory problems. Among these delay, forecast and disruption are related to physical flows of material and product throughout the chain. Forecast risks results from the mismatching between the chain's prediction and the actual demand of the markets. In this paper, we call it demand-side uncertainty just like Lin and Wang (2011). Delay risk occurs when a supplier of a chain cannot deliver the orders on time due to inside problems of its system such as overuse of capacities, impairment of facilities, labor dispute and etc. and disruptions occurs when natural or man-made phenomena such as earthquake, flood, inclement weather, war, labor strike and etc.,by affecting the production and transportation infrastructure, disrupt the flow of material. By ignoring the source of the problem, we call both of them supply-side disruption in this paper. Some of current studies of SCND consider uncertainties under which the SC will operate. Some factors like market demand, disruption of suppliers in on-time delivery of ordered goods and other designing parameters are considered uncertain.in today s business environment, uncertainty is undeniable.due to their decentralized nature, SCs are very vulnerable against uncertainties. As SCs are important part of today s market, managing SCs risks has become an integral part of SCM which involves design of a robust SCN structure and management of product flow throughout the network and make it possible to predict, deal with and recover from the disruption. According to Sarkar et al. (2002), the labor strike in 2002 results in shutting down of29 ports in the West coasts of United States and as the result closure of New United Motor Manufacturing production factory.the recent destructive earthquake in Japan in 2011 made Toyota Motor 18

Company to close 12 assembly plants which results in production loss of 140,000 autos (Talmadge and Kageyama 2011).This problem leads to disruption of Toyota s chain's manufacturing subsystem. In addition to the impairment occurred in production facilities and factories throughout Japan, many Japanese companies faced the problem of supplying the required material as well as fuel and power. In these types of disasters, the disruptions in supplying and manufacturing are the main problems of firms. As mentioned by Norrman and Jansson (2004), firing event in one of the most important suppliers of Ericsson leads to shut down its manufacturing plants for several days. There are many other real-life showing risks, disruption, uncertainty and their effects on SC performance (Cavinato 2004). Interested readers can refer to Jüttner et al. (2003) to have more information about the relationship between categories of risk sources in a network. 5.3. Risk management in SCND literature Many research publications in the field of SCND consider different sources of uncertainties in their modeling. Some of them only optimize the expected value of objective functions (stochastic SCND models) but there are research papers which also consider the performance of the chain in each realization of uncertain part of model and mainly focus on decreasing the deviation between the performances of the chain in different situations which lead to robust SCs. Many works have been done in the literature in the field of managing demand-side uncertainty in SCND. Now we review some of the recent research. Aghezzaf (2005) considered a SCND problem with uncertain demand and proposed a robust optimization approach for the model. Schütz et al. (2009) considered both short-term and long-term uncertainty in demand. They formulated the multi-commodity SC design problem and solved it in two stages: i) strategic location decisions are made in the first stage and ii) operational decisions are made in the second stage. The objective function is to minimize costs, including investment and expected operating costs. They compared stochastic and deterministic models to solve this problem. Their solution technique was a blend of sample average approximation (SAA) and decomposition. Pan and Nagi (2010) formulated a robust optimization model for the problem of SCND when customer demand is uncertain. They assume that there is an organizational web of partners wishing to work as an optimal SC and respond to an existing opportunity in the market. This SC produces a single product and the operation time of each company is one time period. The model tries to choose one partner for each echelon and makes decision about the production plan, inventory level, and amount of backorder. The objective function is minimizing total costs (including cost variability, and a penalty for infeasibility). El-Sayed et al. (2010) also addressed a multi-product SCND problem with uncertain demand. 19

Georgiadis et al. (2011) modeled the problem of SCND in case of multi-product production facilities when production resources, warehouses, distribution centers, customer zones and operating are shared while demand is time varied and uncertain. They modeled uncertainty as some probable scenarios and formulated the problem as a MILP. They also applied standard branch-andbound techniques to achieve global optimum. In order to show the applicability of developed model, a case study concerning the establishment of Europe-wide SC is also introduced. Qi and Shen (2007) introduced unreliable supply in a SC. They considered a multi period problem in which the retailers of the chain order a certain good from the existing supplier in each period and the supplier responds to orders via his facilities. Delivered amount of ordered product to the chain's each retailer includes a probabilistic shortage. Because some portion of it is lost, mistakes and damages are possible. They modeled the delivered amount as the ordered amount and a random variable associated with the supplier s facility which serves the retailer. The objective of their model is maximizing the SC profit. They considered the given product retail price, facility location, inventory, and safety stock costs as well as transportation and penalty costs for retailers. They used a bisection search and outer approximation algorithm to solve the model. Chopra et al. (2007) considered the flow planning throughout a chain with determined network structure. This chain consists of a buyer and two available suppliers. First supplier is cheaper, but prone to disruption and second supplier is completely reliable, but more expensive. In this paper, they considered supply side uncertainty and highlighted the necessity of decoupling recurrent supply risk and disruption risk. Disruption is modeled by scenario and recurrent supply is considered as a random variable with given distribution function. Yu et al. (2009) considered an order splitting problem in a two tiered SC consists of a manufacturer and two available suppliers with disruption probability. First available supplier has lower price but more prone to disruption and in contrast, the second supplier has higher price but lower disruption probability. Schmitt and Snyder (2010) considered the optimal ordering and determining the reserve quantities of a two-tiered SC which involves a firm and its suppliers. One of these suppliers is prone to disruption and has yield uncertainty whereas the second one is completely reliable and always available but more expensive. They modeled and compared this problem in two cases: single-period and multi-period and discussed about the advantages of multiperiod consideration in compare to the other. They also use Branch & Bound technique to solve this problem. There are also works in the literature that consider uncertainty in different parameters of the model. Hwang (2002) considered routing decision in SCND problem with stochastic traveling time. Lowe et al.(2002) considered risk of exchange rate in SCND problem and solve it by a two-phase multi-screening approach. Alonso-Ayuso et al. (2003) considered a multi-period SCND problem 20

with uncertain product net price, raw material procurement cost, and demand.they solvedthe problem by a two stage stochastic programming model. Snyder et al. (2007) proposed a stochastic multi-period model in which demand, cost and distance are all random variables and are described by discrete scenarios. They presented a Lagrangian relaxation based exact algorithm for solving the model. The basic model assumes that the locations of the retailers are known. Azaron et al. (2008, 2009) developed a multi-objective stochastic programming approach for SC design under uncertainty. The uncertain parameters were market demand, supplies of suppliers, processing cost, transportation cost and capacity expansion cost. The objective functions include (i) minimization of the current investment and the expected future cost, (ii) minimization of the variance of the total cost and (iii) minimization of the financial risk. They used goal attainment technique to obtain the Pareto- optimal solutions. Nickel et al. (2012) considered uncertainty of the demand and interest rates in a multi-period multi-commodity stochastic SCND with financial decisions and risk management. The objective of their model is to minimize the total cost (considering the investments made, the revenues, and the transportation costs). Klibi et al. (2010) is one of the most recent review papers in the field of uncertainty in SCND. They introduce SCND as determining location, number and capacity of production-distribution facilities in a chain. The paper posits that there are several sources of uncertainty affecting the design of SCNs and categorizes them from a SCN view point in three main groups including endogenous assets, SC partners and exogenous graphical factors. Klibi et al. (2010) stated that as the result of these potential uncertainties the deterministic SCND models could not guarantee the expected performance of future SCs. In addition, they state that under uncertain environment the level and quality of available information could vary. Depending on the available information when making decisions, three main types of uncertainties occur including randomness, deep uncertainty and hazard. According to the research, although there are some papers on the two first groups, currently there is not any SCND work on hazard. The paper also argues that to ensure long-term profitability and sustainable future value creation for all stakeholders, it is not sufficient to evaluate the performance of a SCN by considering static operational and financial criteria such as economic value added (EVA), service level, market share and etc. As the result, when designing a SCN, the SC future performance needs to be evaluated by considering its robustness, responsiveness and resilience. 5.4. Observations of risk management in SCND literature In the literature, SC risk management is only considered from the perspective of the costs it can 21

result. There is not any study which investigates the advantages of risk management for a chain s competitiveness and profitability. Several strategies are used by SCs to mitigate the negative effects of disruption in physical flows such as having substitutable production and distribution facilities in the chain, having strategic reserves in different tiers of the chain, and keeping extra capacity of the facilities. Imposing these strategies needs some considerations that should be planned in the network design stage. Decision about the number, capacity and location of facilities and the production flow throughout the network are made in the SCND stage.on the other hand, ability of a SC to supply the demand to the customers under all conditions, even in crisis, can increase customer loyalty and increase the attractiveness of the chain in the competitive markets. Being a pioneer in risk management can help a firm to be able to use the crisis condition to capture the market share of the other competitors which do not have any plan to supply their customers in these conditions. Quantifying the risk mitigation ability, as a competitive advantage which influences SC s ability to capture market share, is the first step for competitive SCND with risk mitigation considerations. However, there is a lack of sufficient, precise and practical means to quantify risk and its effects. 6. Other SCND variations Two different production systems are considered in SCND problems: Built-to-stock (BTS) which is the traditional one used in push SCs, and build-to-order (BTO) which is the recent one used in pull SCs. Recently, the success ofhigh-tech companies like Dell, BMW, Compaq and Gateway leads to a great attention to build-to-order SC (BTO-SC) (Ghiassi and Spera, 2003). A lot of companies are now developing BTO-SCs in order to empower themselves for competing in global market and secure their market share. In the process of developing BTO-SC, companies focus on mass customization and e-commerce as well as shortening the cycle of planning and lead-times (Tyan et al., 2003).Kannan et al. (2009) developed an integrated forward logistics multi-echelon distribution inventory supply chain model (FLMEDIM) as well as closed-loop multi-echelon distribution inventory supply chain model (CLMEDIM) for the built-to-order environment. They used particle swarm optimization and genetic algorithm (GA) to solve the problem. The model was applied to two real-world cases in India. Gunasekaran and Ngai (2009) reviewed a number of papers on analyzing and modeling of BTO- SC. Their main goal was to pave the way for effective designing, developing and managing BTO- SC as well as presenting areas of future studies. Lin and Wang (2011) studied SCND under supply and demand-side uncertainties. They modeled strategies such as postponement, centralized/ decentralized stocking and supplier sourcing base and embedded them in SCND in proactive way instead of a permissive reaction to uncertainty. This model was implemented for a BTO-SC. There 22

are a few research papers on BTO-SC and the numbers of studies which explicitly concern the problem of BTO-SCM with modeling are very limited. In addition to objective function and production system, the SCND models differ in several aspects. Some of the models consider a chain for a single product while others discuss multiple products. Some models consider capacitated facilities while others assume non-capacitated facilities. Some models are single period and some are multi-period. 7. Framework of SCND problems The specifications of the SCND problems investigated in the literature are so different from each other. Each of them has some specific assumptions usually imposed by the environmental conditions and each makes special decisions about the network structure of the chain. In this paper, we present a framework in SCND including the major research publications in the literature of the SCND. Figure 2 illustrates this framework and summarizes the strategic questions answered in each section; these strategic decisions are not usually made all together. 23

SCND models Network structure of SC Non-strategic decisions of SC Technology type & production philosophy Objective of model Environmental condition of model Number of facilities Location of facilities Network (Wang, 2009; Gumus et al., 2009) Discrete set (Azaron et al., 2008; Azaron et al., 2009) Network flows Capacity of facilities Quality of facilities (Klibi et al., 2010) Type of technology Determining decomposition point (Gunasekaran and Ngi, 2009; Lin and Wang, 2011) Flow of material among facilities (Shen, 2005; Shen and Daskin, 2005) Transportation mode (Klibi et al., 2010) Inventory decisions (Daskin et al., 2002; Qi and Shen, 2007) IT application (Gunasekaran et al., 2008) Knowledge management (Gunasekaran et al., 2008) (Georgiadis et al., 2011; Lin and Wang, 2011) Lean SCND (Zanjani et al., 2010; Xu et al., 2008)Agile / responsive SCND (Srivastava, 2007; Wang et al., 2011) Green SCND (Buyukozhan and Berkol, 2011; Chaabane et al., 2012) Sustainable SCND Quality of facilities is considered (Azaron et al., 2008; Javid and Azad, 2010) Quality of facilities is ignored (Li et al., 2010; Sawik, 2011) Capacitated facilities are considered (Tsiakis et al., 2001; Shih, 2001) Capacity limitation of facilities is ignored (Daskin et al., 2002; Shen et al., 2003) Uncertainty / disruption of environment (Santoso et al., 2005; Peidro et al., 2009) Competitors / rivals of markets (Rezapour et al., 2011a; Rezapour et al., 2011b) Forward flows (Shen and Qi, 2007; Goh et al., 2007) Backward flows (Shih, 2001; Jayaraman et al., 2003) Both flows (Ko and Evans, 2007; Listes, 2007) Figure 2. Comprehensive specifications of SCND problems 24

REFERENCES Abbasi, M. & Nilsson, F. (2012) Themes and challenges in making supply chains environmentally sustainable. Supply Chain Management: An International Journal, 17(5), 517-530. Agarwal, A., Shankar, R., Tiwari, M.K. (2007) Modeling agility of supply chain. Industrial Marketing Management, 36, 443-457. Aghezzaf, E. (2005) Capacity planning and warehouse location in supply chains with uncertain demands. Journal of the Operational Research Society, 56, 453-62. Alonso-Ayuso, A, Escudero, LF, Garın, A, Ortuno, MT, Perez, G. (2003) An approach for strategic supply chain planning under uncertainty based on stochastic 0-1 programming. Journal of Global Optimization, 26, 97-124. Ayres, R., Ferrer, G., Leynseele, T.V. (1997) Eco-efficiency, Asset Recovery and Remanufacturing. European Management Journal, 15(5), 557-574. Azaron, A., Brown, K.N., Tarim, S.A., Modarres, M. (2008) A multi-objective stochastic programming for supply chain design considering risk. International Journal of Production Economics, 116, 129-138. Azaron, A., Furmans, K., Modarres, M. (2009) Interactive multi-objective stochastic programming approaches for designing robust supply chain networks. Operations Research Proceedings 2008-2009, Part 5, 173-178, DOI: 10.1007/978-3-642-00142-0-28. Baghalian, A., Rezapour, S., Farahani, R. Z. (2013) Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research, 227(1), 199-215. Baker, P.(2008)The design and operation of distribution centers within agile supply chains. International Journal of Production Economics, 111, 27-41. Beamon, B.M., Fernandesa, C. (2004) Supply chain network configuration for product recovery, Production Planning & Control: The Management of Operations, 15(3), 270-281. Bluestein, G., Nuckols, B. Gulf leak eclipses Exxon Valdez as worst U.S. spill. Boston Globe online, 2010. Retrieved from http://www.boston.com/business/articles/2010/05/27/scientists_say_more_gulf_oil_flowing_t han_thought/(accessed on 2 October 2012) Bojarski, A.D., Lainez, J.M., Espuna, A., Puigjaner, L. (2009). Incorporating environmental impacts and regulations in a holistic supply chains modeling: An LCA approach. Computers & Chemical Engineering, 33(10), 1747-1759. Browne, M., Rizet, C., Anderson, S., Allen, J. & Keita, B. (2005) Life cycle assessment in the supply chain: A review and case study. Transport Reviews, 25(6), 761-782. 25

Brundtland, G.H. Our Common Future: The World Commission on Environment and Development. Oxford, U.K., Oxford University Press, 1987. Buyukozkan, G., Berkol, C. (2011) Designing a sustainable supply chain using an integrated analytic network process and goal programming approach in quality function deployment, Expert Systems with Applications 38, 13731-13748. Carter, C. R. & Easton, P. L. (2011) Sustainable supply chain management: Evolution and future directions. International Journal of Physical Distribution and Logistics Management, 41(1), 46-62. Cavinato, J.L. (2004) Supply chain logistics risks: From the back room to the board room, International Journal of Physical Distribution & Logistics Management, 34(5), 383-387. Chaabane, A., Ramudhin, A., Paquet, M. (2012) Design of sustainable supply chains under the emission trading scheme, Internal Journal of Production Economics, 135, 37-49. Chopra S., Reinhardt G., and Mohan U. (2007). The importance of decoupling recurrent and disruption risks in a supply chain. Naval Research Logistics, 54(5), 544 555. Chopra, S., Sodhi, M.(2004) Managing risk to avoid supply-chain breakdown. MIT Sloan Management Review, 46, 53-61. Christopher, M. (2000) The Agile Supply Chain: Competing in Volatile Markets. Industrial Marketing Management, 29(1), 37-40. Christopher, M., Towill, D. (2001) An Integrated Model for the Design of Agile Supply Chains. International Journal of Physical Distribution and Logistics Management 31(4), 235-246. Daskin, M., Coullard, C., Shen, Z.J. (2002) An inventory-location model: formulation, solution algorithm and computational results. Annals of Operations Research, 110, 83-106. Dotoli, M., Fanti, M.P., Meloni, C., Zhou M. (2006) Design and optimization of integrated e- supply chain for agile and environmentally conscious manufacturing. IEEE transactions on systems, man, and cybernetics-part a: systems and humans, 36 (1), 62-57. El-Sayed, M, Afia, N, El-Kharbotly, A. (2010) A stochastic model for forward-reverse logistics network design under risk. Computers & Industrial Engineering, 58, 423-31. Feng, D.Gerald W. E. (2008) A bi-objective reverselogistics network analysis for post-sale service. Computers & Operations Research, 35, 8, 2617 2634. Ferrer, G., Whybark, D.C. The Economics of Recycling, in Guide, V.D.R., Van Wassenhove, Jr., and L.N. eds., Business Aspects of Closed-Loop Supply Chains: Exploring the Issues,Pittsburgh, PA, Carnegie Mellon University Press, 2003. Fishbein, B.K. (2000) Carpet Take-Back: EPR American Style. Environmental Quality Management, 10 (1), 25-36. 26

Fleischmann, M., Kuik, R., Dekker, R. (2002) Controlling inventories with stochastic item returns: a basic model. European Journal of Operational Research, 138, 63-75. Francas, D. Minner, S. (2009) Manufacturing network configuration in supply chains with product recovery. Omega, 37, 757 769. Georgiadis, M.C., Tsiakis, P., Longinidis, P., Sofioglou, M.K. (2011) Optimal design of supply chain networks under uncertain transient demand variations. Omega, 39, 254-272. Ghiassi, M., Spera, C. (2003) Defining the Internet-based supply chain system for mass customized markets. Computers & Industrial Engineering, 45, 17-41. Gimenez, C. &Tachizawa, E. M. (2012) Extending sustainability to suppliers: A systematic literature review. Supply Chain Management: An International Journal, 17(5), 531-543. Goh, M., Lim, J.Y.S., Meng, F. (2007) A stochastic model for risk management in global supply chain networks. European Journal of Operational Research, 182, 164-173. Guillen-Gosalbez, G., Grossmann, I.E. (2009).Optimal design and planning of sustainable chemical supply chains under uncertainty.aiche Journal, 55(1), 99-121. Gumus, A.T., Guneri, A.F., Keles, S. (2009) Supply chain network design using an integrated neuro-fuzzy and MILP approach: A comparative design Gunasekaran, A., Laib,K., Cheng, T.C.E.(2008)Responsive supply chain: A competitive strategy in a networked economy. Omega, 36,549-564. Gunasekaran, A., Ngai, E.W.T. (2009) Modeling and analysis of build-to-order supply chains. European Journal of Operational Research, 195, 319-334. Gunasekaran, A., Spalanzani, A. (2012) Sustainability of manufacturing and services: Investigations for research and applications, International Journal of Production Economics, 140(11), 35-47. Gupta, S., Palsule-Desai, O.D. (2011) Sustainable supply chain management: Review and research opportunities, IIMB Management Review, 1-12. Hormozi, A.M. (1997) Parts Remanufacturing in the Automotive Industry.Productionand Inventory Management Journal, 38(1), 26-31. Hugo, A., Pistikopoulos, E.N. (2005). Environmentally conscious long-range planning and design of supply chain networks. Journal of Cleaner Production, 13(15), 1471-1491. Hugo, A., Rutter, P., Pistikopoulos, A., Amorelli, G., Zoia. (2005). Hydrogen infrastructures strategic planning using multi-objective optimization. International Journal of Hydrogen Energy, 30, 1523-1534. Hwang, HS. (2002) Design of supply-chain logistics system considering service level. Computers & Industrial Engineering, 43, 283-97. 27

Javid, A.A., Azad, N. (2010) Incorporating location, routing and inventory decisions in supply chain network design. Transportation Research: Part E, 46, 582-597. Jayaraman, V., Patterson, R.A., Rolland, E. (2003) The design of reverse distribution networks: Models and solution procedures. European Journal of Operational Research, 150, 128-149. Juttner, U., Peck, H., Christopher, M. (2003) Supply chain risk management: Outlin-ing an agenda for future research, International Journal of Logistics: Research and Applications, 6(4), 197-210. Kahn, C.B.P. Likely to survive spill, but at a cost. Boston Globe online, 2010. Retrieved from http://www.boston.com/news/science/articles/2010/06/02/bp_likely_to_survive_spill_but_at_ a_cost/(accessed on 2 October 2012) Kannan, G., HaqA.N., Devika, M. (2009) Analysis of closed loop supply chain using genetic algorithm and particle swarm optimization. International Journal of Production Research, 47(5), 1175-1200. King, D.S., Mackinnon, A. Who Cares? Community Perceptions in the Marketing of Corporate Citizenship.inAndriof, J., Waddock, S., Husted, B., Rahman, S.S. eds., Unfolding Stakeholder Thinking,London, Greenleaf Publishing, 2002. Klibi, W., Martel, A., Guitouni, A. (2010) The design of robust value-creating supply chain networks: A critical review. European Journal of Operational Research, 203(2), 283-293. Ko, H.J., Evans, G.W. (2007) A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs. Computers and Operations Research, 34, 346-366. Krikke, H., Bloemhof-Ruwaard, J., Wassenhove, L.N.V. (2003) Concurrent product and closed loop supply chain design with an application to refrigerators. International Journal of Production Research, 41(16), 3689-3719. Laszlo, C. The sustainable company: How to create lasting value through social and environmental performance. Washington, Island Press, 2003. Lee, D.H., Dong, M. (2008) A heuristic approach to logistics network design for end-of-lease computer products recovery. Transportation Research Part E: Logistics and Transportation Reviews,44(3), 455-474. Li, X., Chen, Y. (2010) Impacts of supply disruptions and customer differentiation on a partial-backordering inventory system. Simulation Modeling Practice and Theory, 18, 547-557. Li, X., Chung, C., Goldsby, T.J., Holsapple, C.W. (2008) A unified model of supply chain agility: the work-design perspective. The International Journal of Logistics Management, 19(3), 408-435. 28

Lin, C.C., Wang, T.H. (2011) Build-to-order supply chain network design under supply and demand uncertainties, Transportation Research Part B, 45(8), 1162-1176. Listes, O. (2007) A generic stochastic model for supply-and-return network design. Computers and Operations Research, 34, 417-442. Listes, O., Dekker, R. (2005) A stochastic approach to a case study for product recovery network design. European Journal of Operational Research, 160, 268-287. Liu, S. Papageorgiou,L.G. (2013) Multi-objective optimization of production, distribution and capacity planning of global supply chains in the process industry. Omega, 41, 369 382. Lowe, T.J., Wendell, R., Hu, G. (2002) Screening location strategies to reduce exchange rate risk. European Journal of Operational Research, 136, 573-90. Luo, X., Wu, C., Rosenberg, D., David B.D.(2009) Supplier selection in agile supply chains: An information-processing model and an illustration. Journal of Purchasing & Supply Management, 15, 249-262. Majumder, P., Groenevelt, H. (2001) Competition in Remanufacturing. Production and Operations Management, 10(2), 125-141. McMichael F.C., Hendrickson, C.T. (1998) Recycling Batteries. IEEE Spectrum, 35(2), 35-42. Meixell, M. J. &Gargeya, V. B. (2005) Global supply chain design: A literature review and critique. Transportation Research Part E: Logistics and Transportation Review, 41(6), 531-550. Min, H., Ko, H.J., Ko, C.S. (2006) A genetic algorithm approach to developing the multiechelon reverse logistics network for product returns, Omega, 34(1), 56-69. n study. Expert Systems with Applications, 36, 12570-12577. Nagurney, A.,Nagurney, L.S. (2010) Sustainable supply chain network design: a multicriteria perspective. International Journal of Sustainable Engineering, 3(3), 189-197. Neto, J.Q.F., Bloemhof-Ruwaard, J.M., van Nunen, J.A.E.E., van Heck, E. (2008) Designing and evaluating sustainable logistics networks. International Journal of Production Economics, 111(2), 195-208. Nickel, S, Saldanha-da-Gama, F, Ziegler, HP. (2012) A multi-stage stochastic supply network design problem with financial decisions and risk management. Omega, 40, 511-524. Norrman, A., Jansson, U. (2004) Ericsson's proactive supply chain risk management approach after a serious sub-supplier accident. International Journal of Physical Distribution & Logistics Management, 34 (5), 434-456. 29

Ozsen, L., Daskin, M.S., Coullard, C.R. (2006) Capacitated facility location model with risk pooling, Naval Research Logistics, 53(7), 617-626. Pan, F. Nagi, R. (2013) Multi-echelon supply chain network design in agile manufacturing. Omega, 41,969-983 Pan, F., Nagi, R.(2010) Robust supply chain design under uncertain demand in agile manufacturing.computers& Operations Research, 37, 668-683. Peidro, D., Mula, J., Poler, R., VerdegayJ.L. (2009) Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets and Systems, 160, 2640-2657. Pinto-Varela, T., Barbosa-Povoab, A.P.F.D., Novaisa,A.Q. (2011) Bi-objective optimization approach to the design and planning of supply chains: Economic versus environmental performances, Computers and Chemical Engineering, 35, 1454-1468. Pishvaee, M.S., Farahani, R.Z., Dullaert, W. (2010) A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Computers & Operations Research, 37(6), 1100-1112. Pishvaee, M.S., Rabbani, M. (2011) A graph theoretic-based heuristic algorithm for responsive supply chain network design with direct and indirect shipment. Advances in Engineering Software, 42, 57-63. Pishvaee, M.S., Razmi, J. (2012) Environmental supply chain network design using multiobjective fuzzy mathematical programming. Applied Mathematical Modeling, 36(8), 3433-3446. Prater, E., Biehl, M., Smith, M.A. (2001) International supply chain agility - Tradeoffs between flexibility and uncertainty. International Journal of Operations & Production Management, 21(5/6), 823-839. Qi, L., Shen, Z.J. (2007) A supply chain design model with unreliable supply. Naval Research Logistics, 54, 829-844. Qiang, Q. Ke, K. Anderson,T. Dong, J. (2013) The closed-loop supply chain network with competition, distribution, channel investment, and uncertainties. Omega, 41, 186 194. Rezapour, S., Farahani, R.Z., Drezner, T. (2011a) Strategic design of competing supply chain networks for inelastic demand. Journal of the Operational Research Society, 62, 1784-1795. Rezapour, S., Farahani, R.Z., Ghodsipour, S.H., Addollahzadeh, S. (2011b) Strategic design of competing supply chain networks with foresight. Advances in Engineering Software, 42, 130-141. Rondinelli, D.A., Berry, M.A., Vastag, G. (1997) Strategic Programming for Environmental Management: Sonoco s Take-Back Policy. Business Horizons, 40(3), 23-32. 30

Rosenhead, J.,Elton, M., Gupta, S.K. (1972) Robustness and optimality as criteria for strategic decisions. Operational Research Quarterly, 23(4), 413-431. Santoso, T., Ahmed, S., Goetschalckx, M., Shapiro, A. (2005) A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research, 167, 96-115. Sarkar, P., Armstrong, C., Hua, V. (2002) Idling time-the West Coast shutdown is beginning to hurt workers, industries dependent on imports. San Francisco Chronicle. Sawik, T. (2011) Selection of supply portfolio under disruption risks. Omega, 39, 194-208. Schmitt A. J., Snyder L. V. (2010). Infinite-horizon models for inventory control under yield uncertainty and disruptions. Computers & Operations Research, 39(4), 850-862.7 Schütz, P., Tomasgard, A., Ahmed, S. (2009) Supply chain design under uncertainty using sample average approximation and dual decomposition. European Journal of Operational Research, 199, 409-419. Seuring, S., Muller, M. (2008)From a literature review to a conceptual framework for sustainable supply chain management, Journal of Cleaner Production, 16, 1699-1710 Shen, Z.J., Coullard, C., Daskin, M. (2003) A joint location-inventory model. Transportation Science, 37, 40-55. Shen, Z.J. (2005) Multi-commodity supply chain design problem.iie Transactions, 37, 753-762. Shen, Z.J. (2006) A profit maximizing supply chain network design model with demand choice flexibility. Operations Research Letters, 34, 673-682. Shen, Z.J. (2007) Integrated supply chain design models: A survey and future research directions. Journal of Industrial and Management Optimization, 3(1), 1-27. Shen, Z.J., Coullard, C., Daskin, M. (2003) A joint location-inventory model. Transportation Science, 37, 40-55. Shen, Z.J., Daskin, M. (2005) Trade-offs between customer service and cost in integrated supply chain design. Manufacturing and Service Operations Management, 7, 188-207. Shen, Z.J., Qi, L. (2007) Incorporating inventory and routing costs in strategic location models. European Journal of Operational Research, 179(2), 372-389. Shih, L.H. (2001) Reverse logistics system planning for recycling electrical appliances and computers in Taiwan. Resources, Conservation and Recycling, 32, 55-72. Shu, J., Teo, C.P., Shen, Z.J. (2005) Stochastic transportation-inventory network design problem. Operations Research, 53, 48-60. 31

Snyder, L.V., Daskin, M.S., Teo, C.P. (2007) The Stochastic location model with risk pooling, forthcoming. European Journal of Operational Research, 179 (3), 1221-1238. Srivastava, S.K., (2007) Green supply chain management: A state of the art literature review. International Journal of Management Reviews, 9(1), 53-80. Srivastava, S.K. (2008) Network design for reverse logistics, Omega, 36, 535-548. Talmadge, E., Kageyama, Y. (2011) Japan Earthquake 2011: Battered Country Faces Threat Of Nuclear Meltdown As Death Toll Rises (http://www.huffingtonpost.com/2011/03/12/japan-nuclear-plant-explosion_n_834867.html) (accessed on 24 September 2012) Toffel, M.W. (2004) Strategic Management of Product Recovery. California Management Review, 46(2), 120-141. Tomlin, B.(2006)On the value of mitigation and contingency strategies for managing supply chain disruption risks. Management Science 2006, 52(5), 639-57. Tsiakis, P., Shah, N., Pantelides, C.C. (2001) Design of Multi-echelon Supply Chain Networks under Demand Uncertainty. Industrial and Engineering Chemistry Research, 40, 3585-3604. Tyan, JC, Wang,FK., Du, TC. (2003) An Evaluation of freight consolidation policies in global third party logistics. Omega, 31, 55-62. Wang, F, Lai, X, Shi, N. (2011) A multi-objective optimization for green supply chain network design. Decision Support Systems, 51, 262-269. Wang, H.S. (2009) A two-phase ant colony algorithm for multi-echelon defective supply chain network design. European Journal of Operational Research, 192, 243-252. Widger, D. Going green to recruit and retain employees.greenbiz.com, 2007.Retrieved fromhttp://www.greenbiz.com/blog/2007/11/09/going-green-recruit-and-retain-employees. Wilkerson, T. (2005) Can One Green Deliver Another? Harvard Business School Publishing Corporation (available at: http://www.supplychainstrategy.org). Xu J., Liu Q., Wang R. (2008) A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor. Information Sciences, 178, 2022-2043. You, F, Grossmann, IE. (2008)Design of responsive supply chains under demand uncertainty. Computers and Chemical Engineering, 32, 3090-3111. Yu, H., Zeng, A.Z., Zhao, L. (2009) Single or dual sourcing: decision-making in the presence of supply chain disruption risks. Omega, 37, 788-800. 32

Zanjani, M.K., Ait-Kadi, D., Nourelfath, M. (2010) Robust production planning in a manufacturing environment with random yield: A case in sawmill production planning. European Journal of Operational Research, 201, 882-891. 33