A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design

Journal of Cleaner Production xxx (2015) 1e18

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A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design Kiran Garg a, Devika Kannan b, *, Ali Diabat c, P.C. Jha a a

Department of Operational Research, University of Delhi, Delhi, India Department of Technology and Innovation, University of Southern Denmark, Odense M, Denmark c Department of Engineering Systems and Management, Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 19 February 2014 Received in revised form 28 January 2015 Accepted 2 February 2015 Available online xxx

Imposition of strict environmental protection acts and the imperative need of best possible allocation of resources have given birth to the concept of “low carbon logistics.” Environmental laws force the manufacturers to extend their existing supply chains to form a closed loop supply chain (CLSC) through the setup of an efficient recovery system. In this paper, we attempt to deal with the environmental issues presented in the design of CLSC networks. The CLSC network proposed in the paper consists of four echelons in the forward chain and five echelons in the backward chain. To consider the environmental issues in the proposed CLSC network, we formulate a bi-objective integer nonlinear programming problem, and in order to solve it we propose an Interactive Multi- Objective Programming Approach Algorithm. This model determines the optimal flow of parts and products in the CLSC network and the optimum number of trucks hired by facilities in the forward chain of the network. A numerical experimentation of the proposed model to validate the applicability of the model is done with the help of data from a real life case study. The case presented in the paper is based on a geyser manufacturer, and its application on the model provides us with the underlying tradeoffs between the two objectives. The model also results in a very interesting fact that with the implication of the extended supply chain, a firm can create a green image of their product which eventually results in an increase in their demand while significantly reducing their usage of transportation in both directions. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Low carbon logistics Closed loop supply chain Recovery system Sustainable supply chain Electrical goods

1. Introduction The inevitable extraction of the earth's natural resources due to its population's increasing demand will surely leave the earth exposed and exceedingly expensive for future generations. Because many of these resources are consumed by supply chains, logistics and supply chain managers are well aware that the integration of 3R's (reduce, reuse, and recycle) cannot be looked at as afterthoughts; implementing the 3R's into their existing collection systems for end-of-use (EOU) or end-of-life (EOL) products is mandatory. The collection of used products integrates the recovery process in the existing supply chain. The integration of reverse logistics into the existing supply chains becomes mandatory, which results in a closed loop supply chain (CLSC) (Santos et al., 2013; Jindal and Sangwan, 2013; Abdallah et al., 2011; Li et al., 2014;

* Corresponding author. E-mail address: [email protected] (D. Kannan).

Pochampally et al., 2009). Organizations also prefer a CLSC because of stringent policies regarding landfills and the high cost of environmentally safe e-waste disposal. A CLSC may also help companies to gain profit by selling carbon credits that have exceeded their carbon cap (Labatt, 2007; Abdallah et al., 2012) and to enhance the company's environmental image (Paksoy et al., 2011). Typically, most of the elements of EOL/EOU electrical and electronic products are sent to landfills, which is not a viable solution because these products contain both toxic and valuable materials. The same applies to products pertaining to other industries, such as the automotive one (Diabat et al., 2013a,b). Recycling and remanufacturing of EOL/EOU products helps to retrieve valuable materials (Kannan et al., 2012). Capturing these products significantly reduces both the demand for raw material and concerns over ewaste management. The recovery process is already considered a useful approach towards resource conservation in the western world. However, developing countries have not yet adopted adequate capture strategies, so EOU/EOL waste products remain a

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Please cite this article in press as: Garg, K., et al., A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.02.075

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challenge for these countries' population and environment (Williams et al., 2008). The import of used products needs to be stringently prohibited and laws regarding waste management by the manufacturers through reverse logistics (RL) is a prerequisite (Govindan et al., 2014a,b,c,d). Rogers and Lembke (1999) define RL as“The process of planning, implementing, and controlling the efficient, cost-effective flow of raw materials, in-process inventory, finished goods, and related information from the point of consumption to the point of origin, for the purpose of recapturing value or proper disposal.” In RL, the original equipment manufacturer (OEM) is responsible for collecting used products from customers and dismantling them into their components for rework, reuse and recycle, or disposal (Govindan et al., 2013; Almur et al., 2012; Govindan and Popiuc, 2014; Sasikumar and Kannan, 2009). In a CLSC, the forward supply chain is used to fulfill the demand of end customers, whereas RL handles the flow of EOL and/or EOU products from users and reduces the burden of waste on earth (Kumar et al., 2014). The individual management of forward and reverse supply chains may lead to sub-optimal designs whereas the integration of RL within the existing supply chain is more cost effective (Sasikumar and Kannan, 2008a,b; Lee and Dong, 2008; Verstrepen et al., 2007; Govindan et al., 2015a). Also, utilizing second hand products in the established secondary market of the developing countries can increase revenue and may help appropriately manage the returned products that are still in their working life. The successful establishment of a CLSC network assists in the execution of environmentally safe practices by: a) designing modular products using recovered parts and recycled materials, and b) adopting environmentally friendly and efficient transportation distribution systems (Amin and Zhang, 2013). 35% of total greenhouse gas emissions are due to the use of heavy and light vehicles in the supply chain (Paksoy et al., 2010). Thus, there is an urgent need to optimize transportation efficiencies in supply chains to significantly reduce greenhouse gas emissions. To entrench environmentally safe policies in the company's operations, the integration of an optimally designed CLSC network, minimizing transportation related activities in the extended network, is obligatory. To design an environmentally viable CLSC, decision makers need to ensure that forward direction shipments contain large batches, while transportation quantities in the return direction remain fairly low. Hence, using a full transport load is advisable because it also fetches freight discounts to the company and minimizes the number of vehicles used. With the reverse direction, minimizing the transport load of the vehicle appears to be an apt policy. This paper integrates decisions made at the strategic level (such as designing the CLSC network) with decisions made at the tactical level (related to transportation activities). To abstract the real world application from the problem, a mathematical modelling approach is used. This model represents the problem of optimizing revenue while simultaneously decreasing transportation-related costs in the proposed CLSC by attending to the number of vehicles used and the distance travelled. Our study is influenced by the management of returned geysers (25 L) in Delhi and the National Capital Region. For a broader insight on the Indian industry the reader is referred to the work of (Diabat et al., 2014). A bi-objective integer nonlinear programming problem is formulated, and, in order to solve it, an interactive multi-objective programming approach algorithm is proposed as a methodology. The paper proceeds as follows. Section 2 reviews the existing literature related to the problem. Section 3 describes the necessity of the establishment of the CLSC network in terms of the problems faced by the company. A mathematical formulation of the problem is given in Section 4 along with the solution methodology. Section 5 validates the model through company-provided data. Section 6 discusses the model results, and

Section 7 concludes the paper and suggests future research directions. 2. Literature review CLSC has increasingly gained the interest of researchers and practitioners over the past three decades. Dealing with environmental issues in supply chains has been an area of great concern. In addition, new solution methodologies that deal with difficult multi-objective problems have been equally important. Thus, in this section we examine the existing literature relevant to CLSC design, the Green Supply Chain (GrSC), and the solution methodologies adopted to this point, and we categorize Section 2 accordingly: CLSC design and planning, the Green Supply Chain, and Solution methodologies adopted to solve CLSC models. 2.1. CLSC design and planning Creating a recovery system from scratch could be very costly; therefore, Mutha and Pokharel (2009) proposed the allocation of a certain portion of the warehouse, reprocessing units, and factories of forward chains for the recovery process instead of establishing a whole new setup. They also presented an elaborated discussion over the location of the facilities of the reverse logistics (RL) and factors that significantly influence RL setup. Sasikumar and Kannan (2008, 2009) presented a comprehensive review of the existing literature on the CLSC optimization and network design. Wang and Hsu (2010) proposed the design of a closed loop logistics system through the integration of the forward and reverse logistics in view of decisions regarding long term and steady state logistics. To maintain the originality of the integrated problem, they used a heuristic solution approach. Kannan et al. (2010) proposed a multiperiod closed loop supply chain network for the optimum usage of recovered material in terms of lead recovered from lead-acid batteries. Their purpose was to develop a multi-echelon, multi-period, and multi-product CLSC to determine optimum distribution and inventory level decisions through a heuristics-based genetic algorithm (GA). On the other hand, (Diabat et al., 2015) address the single echelon case for both the forward and backward logistics of a closed-loop location-inventory problem, but develop an exact twophase Lagrangian relaxation to solve it. Akçalı and Centinkaya (2011) reviewed the work done on CLSC and classified the research on the basis of deterministic and stochastic modelling approaches and also on the parameters that could significantly affect the complexity of the CLSC models. Recently, Ramezani et al. (2013) proposed a stochastic multi-objective model for the integrated forward and reverse supply chain network under uncertain environments. In their study, uncertainty referred to the return rate of used products. They aimed at maximization of profits, customer service levels in both forward and reverse networks, and sigma € quality levels by minimizing defects in raw materials. Ozkır and Bas¸lıgil (2013) proposed a multi-period, multi-commodity and capacitated CLSC network design with the help of a multi-objective optimization model. Georgiadis and Athanasiou (2013) worked on the design of CLSC with remanufacturing for long-term capacity planning in the reverse channel. Amin and Zhang (2014) proposed a mixedeinteger linear programming model to configure a CLSC network for a copier remanufacturing case. Low et al. (2014) presented a product structure-based integrated life cycle analysis (PSILA) with the help of cost modelling in closed loop production system. Devika et al. (2014) proposed a mixed integer linear programming model to design a CLSC network to capture the triple bottom line of the sustainability. They considered recovering, remanufacturing, recycling, and disposal facilities under treatment centers of the reverse logistics network. Solaimani and Kannan

Please cite this article in press as: Garg, K., et al., A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.02.075

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(2015) proposed a hybrid algorithm based on particle swarm optimization (PSO) and genetic algorithm (GA) for a CLSC network design problem. There exist several other studies addressing risk issues, dynamic pricing and collection issues (Soleimani and Govindan, 2014; Sun et al., 2013; Wei et al., 2015; Soleimani et al., 2014) in a CLSC. 2.2. Green supply chain Interest in the greening of the existing supply chain by incorporating recovery processes has attracted the attention of researchers because of concerns over global warming and the rapid growth of e-waste (Abdallah et al., 2013). A primary objective of a Green supply chain (GrSC) should be to minimize the harmful effects of the chain's various activities on the environment (Al Zaabi et al., 2013). Krikke et al. (2003) considered minimization of facility set-up, processing, and distribution costs in the CLSC network design, while designing a GrSC with support from both product design and logistics networks. Sarkis (2003) provided a strategic decision framework with the help of an analytical network process for making decisions within the GrSC. Srivastava (2007) presented a review article of the published literature on GrSC and, on the basis of the review, he classified various metrics of environmental performances. Integration of the environmental goals into the manufacturing system through a CLSC network design was recommended by Winkler (2011). Paksoy et al. (2011) proposed the promotion of reusable products to decrease the environmental burden and simultaneously to decrease the operational costs of the network. They proposed a multi-objective model to consider tradeoffs between the cost of transportation and the resulting emissions. Fahimnia et al. (2013) also presented a comparative study of the supply chains influenced by the environment and cost issues. According to them, the development of a green supply chain includes transportation optimization and formation of closed-loop systems to preserve resources. There exist several performance and modeling studies (Charkha and Jaju, 2014; Yihui et al., 2014; Ramaa et al., 2013; Govindan et al., 2014a,b,c,d; Brandenburg et al., 2014; Govindan et al., 2014a) and empirical studies (Govindan et al., 2015b; de Sousa Jabbour et al., 2013; Diabat and Govindan, 2011; Govindan et al., 2014b; Jabbour et al., 2014; Mathiyazhagan et al., 2013a,b; Muduli et al., 2013a,b; Rostamzadeh et al., 2015; Xu et al., 2013) related to traditional, reverse, closed loop, green, and sustainable supply chains. Brandenburg et al. (2014) presented a review on the development and directions of mathematical modelling considering environmental issues/factors in supply chains. Govindan et al. (2014a,b,c) provided an overview of the current status and underlying opportunities in the development of eco-efficient supply chain management through green supply chain modelling. 2.3. Solution methodologies adopted to solve CLSC models Past researchers and practitioners used and proposed various solution methodologies to deal with CLSC designing and planning problems. Salema et al. (2006) used CPLEX solver for the proposed CLSC design and planning problem. Lu and Bostel (2007) used a Lagrangian relaxation algorithm to solve the proposed mixed integer linear programming problem. The problem was influenced with the introduction of remanufacturing in a CLSC network. Wang and Hsu (2010) developed a revised spanning-tree based genetic algorithm for the proposed integrated CLSC model. Amin and Zhang (2012) considered Life Cycle Assessment through return recovery pairs and product life cycle by proposing a mixed integer linear programming model. The model is solved by GAMS. Several linguistic based algorithms and soft computing based algorithms

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were also used to solve several supply chain related problems (Mahmoudabadi and Seyedhosseini, 2014; Bansal et al., 2014; Pramanik et al., 2014; Salimifard and Raeesi, 2014; AmadoreFontalvo et al., 2014; Saleem et al., 2013; Soleimani and Kannan, in press). Devika et al. (2014) proposed three metaheuristic approaches to solve the multi-objective problems which were based on adapted imperialist competitive algorithms and variable neighborhood search algorithms. A large number of mathematical transformations of real problems do not necessarily result in single objective models, and the most difficult obstacle in solving a multi-objective model is the application of available mathematical techniques. However, decision makers want to make tradeoffs between disparate and conflicting design objectives; hence, researchers worked persistently on the solution methodologies of multi-criteria problems. Most of the multi-criteria decision making (MCDM) and multi-objective decision making (MODM) problems consist of large numbers of decision variables, constraints, and objectives (Dubey et al., 2013). The extension of Linear Programming to multi-objective optimization techniques and to goal programming is to deal with multiple and conflicting objectives (Chang, 2011). Other methodologies to solve multi-objective programming problems were also discussed by a number of authors. For example, Marler and Arora (2010) provided the significance of the Pareto optimal analysis of the objective functions. The other technique to solve the multiobjective problem is the interactive approach, an approach that attempts to generate the best compromise solution by progressively articulating the preferences of a decision maker facing multiple criteria with complex tradeoffs (Aksoy et al., 1996). Recently, Emam (2013) used the interactive approach for solving a bi-level multi-objective fractional programming problem. Because many models related to CLSC result in multi-objective programming problems, theories related to multi-criteria decision making in CLSC are prosperous. Pati et al. (2008) formulated a multi-objective model for decisions in a multi-item, multi-echelon, and multi-facility framework to determine the facility location, route, and flow of different varieties of recyclable wastepaper. They used a mixed-integer goal programming approach to solve the model. Pishvaee et al. (2010) used a dynamic search mechanism to solve a bi-objective mixed-integer nonlinear programming model for a capacitated closed-loop network design through a memetic € algorithm. Ozkır and Bas¸lıgil (2013) used GAMS/Baron solver to solve the proposed fuzzy multi-objective optimization model. € Ozceylan and Paksoy (2013) considered the capacity, demand constraints, return rate, and objectives under a fuzzy environment and followed a fuzzy multi-objective model (FMOM) to solve the proposed mixed integer fuzzy mathematical model through CPLEX. 2.4. Research gap and highlights Research in this article is influenced by issues concerning the environmental impact of CLSC network design. Earlier researchers considered environmental issues in the CLSC network design, but they did so in different manners. Kannan et al. (2009) integrated the forward and reverse logistics primarily to see the impact of designing the CLSC network with regard to the end consumer in built-to-order environments and secondly to reduce as much as possible monetary and resource investments in inventory. To support their finding they proposed multi-echelon inventory distribution models for reverse and closed loop supply chains separately. They made use of genetic algorithms to solve their proposed models. Paksoy et al. (2010) emphasized the reuse of recovered and recycled material while considering the minimization of carbon emissions of transporting vehicles through multi-objective mixed integer linear programming problems. They used Lindo 6.1 to solve

Please cite this article in press as: Garg, K., et al., A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.02.075

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the model. Paksoy et al. (2011) analyzed the trade-offs between the various operation and environmental performances measures of CLSC using a single objective mixed integer programming problem. They also presented the result of their studies for different scenarios; two of them are the effect of change in demand of product type (type varies on the basis of the recyclability) and the effect of change in transportation costs on the environmental and economic factors of the proposed CLSC. Ramezani et al. (2013) presents a stochastic multi-objective model for forward/reverse supply chains to maximize profit and responsiveness while minimizing defective parts from the supplier under uncertain environments. He evaluated the model using a real life case study, using scenarios that included financial risks to overcome. The formulation of the model is influenced by global warming. Diabat et al. (2013a,b) evaluated

the impact of carbon emissions on the design of CLSC network while considering carbon trading in a multi-echelon, multi-commodity facility network. The comprehensive review of Kannan et al. (2014) revealed that the existing literature on greening the supply chain through RL/CLSC considered only greenhouse gas emissions as an environmental issue but did not depict a way to overcome this problem. They also mentioned that there is a gap in decision making through multi-objective approaches and a need to propose more applicable methods to analyze multi-objective problems. Table 1 summarize some of the characteristics of some important earlier resources to establish the existing gap. The existing models do not mention the problem of greening the CLSC in the way it is introduced in this study by considering the different modes of transportation utilized in forward and reverse chains. The

Table 1 Characteristics of some important studies relevant to this research. Paper

Area

Aim of research

Methodology

Result analysis

Kannan et al. (2009)

Integrated forward and reverse logistics

Genetic algorithm and particle swarm optimization

Closed loop supply chain model is more beneficial to the customer in terms of cost aspects

Paksoy et al. (2010)

Green supply chain

To develop a forward logistics closed loop multi-echelon distribution inventory supply chain model for the built-to-order environment Minimization of CO2 emissions

Solver Lindo 6.1

Wang and Hsu (2010)

Closed loop logistics

Reuse of the recycled material can save a good amount of money for the manufacturer The proposed genetic algorithm is able to solve closed loop mode more efficiently than LINGO and CPLEX

Paksoy et al. (2011)

Closed loop supply chain

Amin and Zhang (2012)

Closed loop configuration

Ramezani et al. (2013)

Forward/reverse logistic network design

€ Ozkır and Bas¸lıgil (2013)

Closed loop supply chain

Georgiadis and Athanasiou (2013)

Reverse channel of closed loop supply chain

Diabat et al. (2013a,b)

Carbon trading in closed loop supply chain

€ Ozceylan and Paksoy (2013)

Closed loop supply chain

Devika et al. (2014)

Triple Bottom Line through closed loop supply chain

Our Study

Closed loop supply chain

Comparison of the results of a closed loop facility location model for the proposed algorithm with the existing methodology Trade off between the of various operation and environmental performances measures in a closed loop supply chain To develop a general closed loop network based on product life cycle and returnerecovery pairs To evaluate the systematic supply chain configuration maximizing the profit, customer responsiveness, and quality of the logistic network Maximizing satisfaction level of trade, satisfaction degrees of customers, and total CLSC profit function To choose among the strategies either early large-scale investments to benefit from economies of scale or a flexible strategy of low volume but more frequent capacity expansions To analyze the effect of carbon trading and procurement options on the cost and configuration of the closed loop supply chain To incorporate logistics manager aspiration level for the fuzzy objective and uncertainties in the capacity, demand and return rate through a fuzzy multi-objective model To capture the multi-dimensionality of sustainability through a multi-objective optimization model for a closed loop supply chain network

To control the environmental issues (increasing usage of transportation activities) while extending the traditional supply chain to form a CLSC

Spanning tree based genetic algorithm Lindo 6.1

Primarily measure cost while capturing environmental aspects

Generalized Algebraic Modeling System (GAMS)

Consideration of commercial returns while satisfying the end customer demand decreases the manufacturer's cost Trade off between the objectives through Pareto optimal curve and calculated financial risk relevant to them (1) Effect of return's quantity on CLSC problems, (2) illustration of quality indicators to describe return quality Flexible policies are good alternatives to large-scale capacity expansions considering the actual pattern of endof-use product returns

Pareto optimal configuration

GAMS/Baron solver

Simulation-based system dynamics optimization approach GAMS e CPLEX

GAMS e CPLEX

Metaheuristics algorithms based on imperialist competitive and variable neighborhood search Interactive MultiObjective Programming approach algorithm

If selection of suppliers for procurement is carbon intensive, then remanufacturing can significantly lower the carbon footprint Facilitiates a fuzzy decision-making process to adjust the fuzzy data and related parameters to obtain acceptable results Impact of social factors on opening costs during both opening and operating, and it also shows that increasing the weight of cost function leads to an increase in lost days: this model reduces or increases the number of job opportunities and the work's damages simultaneously Trade offs between the two objectives of the problem and the impact of the closed loop supply chain on the economical performance of the company

Please cite this article in press as: Garg, K., et al., A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.02.075

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earlier papers do not consider the use of a minimal number of vehicles (using full transport load transportation in the forward chain and lighter transport loads in the reverse chain) as a means to lower the impact of the extended supply chain on the environment. To account for all of these issues, we propose a bi-objective mixed integer nonlinear programming problem in this paper. We also propose an Interactive Multi-Objective Programming Approach Algorithm to solve the multi-objective problem.

3. Problem definition XYZ Pvt. Ltd., a Delhi based organization founded in 1980, is one of the leading manufacturers of electrical appliances such as fans, irons, home appliances, and geysers. Due to the limited flexibility of the operations managers and in order to maintain the confidentiality of the company's expertise, we do not identify the company here. Instead, we refer to this firm as XYZ Pvt. Ltd. The manufacturing units of the company are well equipped with current technologies and the firm's infrastructure, operations, and policies are regularly reviewed to maintain the motive of the company. This company serves its customers by providing the best quality electrical appliances and excellent after sales service. The after sales service policy of the firm extends for three years free service for newly installed geysers. Sometimes they need to replace spare parts in the appliances, which can be expensive. Moreover, with the age of the installed geysers, the service cost is expected to increase and the implementation of e-waste management laws will likely force the manufacturer to think about alternative policies regarding the old units while continuing with their after sales service policies. In 2007 they began a take back program through an “exchange programme or buy back scheme,” which enables the company with economic benefits of discontinuing the costly service of old units. The company was not able to achieve a waste-free environment, because the usable parts they extract from remaining old units are disposed of at a landfill. With the implementation of e-waste laws in India in 2011, the disposal fee at landfills is exceedingly high and the environmentally safe treatment of hazardous substances became mandatory. Therefore, there is a need to design a CLSCN for the company to estimate the feasibility of reclaiming the components and recycled material from their manufactured EOL or EOU geysers. Old units in the collection center need to be reprocessed by an effective set-up of reverse logistics. Reverse logistics begin with the collection of EOL and EOU products. Returns can be collected at regional collection centers through various possible means and then they must undergo a quality check at the company's owned collection center. From there, returns are consolidated at a dismantling center.

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At the dismantling center, used or EOL geysers are disassembled into their components and then these components are segregated into usable and non-usable categories. Usable components are refurbished and can be sold in the secondary market. Components that cannot be recovered from the returns and those that are discarded due to poor quality are moved to either decomposition centers for material recovery or to the disposal site after treatment. With the extension of these facilities in the company's existing supply chain, the company would be able to sustain a waste-free policy. However, with the extension of the network, the company might face the problem of effective management of transportation activities. Increasing vehicle usage in the network would negatively impact the environment. As a result, the managerial motivation with the establishment of the CLSCN is the maximization of the revenue and the minimization of the transportation related activities.

4. Model description and methodology 4.1. Model formulation To deal with the above problem faced by the company, we configured a CLSC network for the company, as shown in Fig. 1. Network facilities can be classified into two groups: namely, forward supply chain facilities and reverse supply chain facilities. The forward supply chain, which is the same as the traditional supply chain of the company, consists of raw material supplier facilities, manufacturing facilities, and distribution centers to serve their end customers. The reverse supply chain consists of six facilities: regional collection centers, a centralized dismantling center, repair centers, a spare market zone, the decomposition center, and the disposal site location. The forward supply chain begins with the procurement of raw material from suppliers. Plant facilities are well equipped with required technology and responsible for manufacturing various components and then assembling them into products. From there, finished products move towards the end customers via distribution centers and customer zones. In the reverse chain of the proposed CLSC network, returned products are collected from their users through a take back scheme. Users will be paid incentives for returning their end of life (EOL) used products at the companyoperated collection center. Value can be recovered by dismantling returned products into the components demanded in the spare market. Thus, these returned quantities must be transhipped to the centralized dismantling center. The dismantled components will be inspected for their quality and sorted out for refurbishment and recycling. Reusable components will be repaired and sold in the spare market, while the remaining components are conveyed

Fig. 1. Closed loop logistics network.

Please cite this article in press as: Garg, K., et al., A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.02.075

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either to the decomposition center to extract recyclable materials or to the disposal site. In the forward supply chain, large batches are needed to be consolidated; therefore, the company should go for a full transport load mode of transporting vehicles. Conversely, while in the reverse supply chain, the amount needed to be transported is fairly low; hence, a lighter transport load mode of transportation is apt. In forward supply chains, the company hires trucks from Sigma Logistics (name changed), a 3PL provider. Sigma Logistics maintains a fleet of different types of trucks with different capacities. Renting any truck depends on its capacity and on its engine efficiency. The company aims to minimize the number of trucks to be hired because they are aware of their impact on the environment. This policy may be costlier due to strict pollution norms and expensive maintenance of big vehicles. The reverse supply chain is different; all the stages, except the collection center, are single echelon. The transportation of returned products from collection centers to the centralized dismantling center is to be carried out by companyowned small trucks. In view of minimizing the number of trips and distance travelled by the trucks, the routing of trucks between the collection center and the centralized dismantling center is to be analyzed with the following limitations: 1) the collection of returned products from each collection center, 2) the tour of any truck will start and end at the centralized dismantling center and it will cover one or more collection centers, and 3) vehicle capacity limitations and tour length limit will be respected. Seeing all these issues, a model depicting the requirement of the proposed CLSC for the company is formulated. A model corresponding to the proposed multi-echelon CLSC is configured for a single product and a single period to determine the optimum flow of material, product and parts in the network, while maximizing the total profit and minimizing the number of trucks hired for the forward supply chain. Thus, the problem becomes a multi-objective problem. The first objective would be maximizing profit and the

Sets: I J K L C p q S f D R M VI VJ VI T Cost parameters: Purir Prodmj Assj OPk BB Dis Repm Decr H ni H nj H nk TC pq TCm TCms pd TCm TCrdi

second one is minimizing transportation related activities in the forward chain. The second objective is describing the environmental issues raised while designing the CLSC network, as these issues indirectly signify the minimization of carbon footprints for the company. When the traditional supply chain of the firm is extended to include recovery processes for EOU/EOL products, the carbon footprint and the usage of transporting vehicles would certainly increase. To lower this impact, minimizing the number trucks used in the forward chain becomes mandatory. Because the forward chain of the network needs the large number of trucks to satisfy their service level, and because there are controlling factors responsible for the high carbon footprint of the company, the firm may not be able to survive economically. Moreover, during the course of model formulation, the following assumptions are postulated: 1. Demand at customer end is deterministic; there is no shortage. 2. Facility locations are known a priori and they are fixed. 3. The flow of products, parts, and materials can occur only in between two consecutive stages; inter-stage flow is not allowed. 4. All cost parameters are deterministic and all operations of CLSC are to be carried under capacity limitations. 5. Components and materials which cannot be recycled are sent to the disposal site after treatment. Pre-disposal treatment cost is assumed to be included in the disposal cost. 6. Transportation cost of collecting returned products from collection centers is borne by the company and is directly proportional to the total distance covered. 7. Set-up cost of facilities is considered to be a part of the operations cost of the respective facilities.

4.2. Model description

Set of raw material supplier, indexed by i, i ¼ 1, 2… I Set of manufacturing plant, indexed by j, j ¼ 1,2,…J Set of distribution center (d/c), indexed by k, k ¼ 1,2..K Set of first market customer zone, indexed by l, l ¼ 1,2..L Set of collection centers (CC), indexed by c, c ¼ 1, 2.., C Index for dismantling center Index for repairing center Set of spare market zone, indexed by s Index for disposal sites Index for decomposition center Set of raw materials, indexed by r, r ¼ 1, 2... R Set of components, indexed by m, m ¼ 1, 2... M Set of vehicles at node i, i 2 I, indexed by vi, vi ¼ 1,2..VI Set of vehicles at node j,j 2 J, indexed by vj, vj ¼ 1,2..VJ Set of vehicles at node k, k 2 K, indexed by vk, vk ¼ 1,2..VK Set of vehicles at manufacturer, indexed by t, t ¼ 1,2,.. Per unit purchasing cost of rth material from ith supplier Production cost of mth component at jth plant Assembling cost of a product at jth plant Per unit operating cost at kth distribution center Incentive paid for a return (fixed regardless of the condition) Per unit dismantling cost Per unit repairing cost of mth component Cost of producing rth raw material from decomposition process Hiring cost of a vehicle vi at supplier nodes Hiring cost of a vehicle vj at plant nodes Hiring cost of a vehicle vk at distribution centers node Per mile transportation cost from collection centers to dismantler Cost of transporting a unit of mth component from dismantler to repair center Cost of transporting a unit of mth component from repair center to spare market zone Cost of transporting a unit of mth component from dismantler to decomposition center Cost of transporting per kg of material r from decomposition center to supplier i

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K. Garg et al. / Journal of Cleaner Production xxx (2015) 1e18

7

(continued ) Other parameters Dl Demand of consumers in the market zone l Dsm Demand of mth component in spare market s a Rate of return W Weight per unit of finished and packed product bm Utilization rate of mth component in the product gm Repairing rate of mth component hr Recycling ratio of rth raw material after decomposition r mm Utilization rate of rth material per unit of mth component Wr Per unit weight of rth material SPl Unit selling price of product at market zone l RSPm Unit selling price of mth component in the spare market s Revr Revenue generated by a unit of rth material due to recycling Capacity of ith supplier for supplying rth material Rcapir Pcapj Production capacity of plant j Capvi Capacity (in kg) of vith truck at supplier's node Maxvi Maximum no of vith type of truck available at supplier's node NCapvj Capacity (in unit) of vjth truck at plant's node WCapvj Capacity (in kg) of vjth truck at plant's node Maximum number of vjth type of truck available at plant's node Maxvj NCapvk Capacity (in unit) of vkth truck at d/c node WCapvk Capacity (in kg) of vkth truck at d/c node Maxvi Maximum number of vkth type of truck available at d/c node Dcc Distance between any two collection centers Dpc Distance between dismantler and collection center c  1; if collection center c is opened to collect return goods from market zone l Alc ¼ 0; otherwise  1; if market zone l to be catered by distribution center k Bkl ¼ 0; otherwise Decision variables: ni Xijr Quantity of material r shipped from supplier i to plant j via truck vi n Xjkj Quantity of product shipped from plant j to d/c k via truck vj nk Xkl Quantity of product shipped from d/c k to customer l via truck vk Xlc Quantity of used product returned from customer market zone l to collection center c Xc Quantity of product shipped from collection center c to dismantler center XPQm Quantity of mth component shipped from dismantler to repairing center q XQSm Quantity of mth component shipped from repairing center q to spare market s XPDm Quantity of mth component shipped from dismantler to decomposition center XDIir Quantity of rth material shipped from decomposition center to supplier i XDFr Quantity of rth material shipped from decomposition center to disposal site Nvi Number of type of vehicle vi hired by ith supplier Nvj Number of type of vehicle vj hired by jth plant Nvk Number of type of vehicle vk hired by kth d/c  1; if a transportation link is established between raw material supplier i and plant j; via mode vi ¼ Lvi ij 0; otherwise  1; if a transportation link is established between plant j and d=c k; via mode vj vj Ljk ¼ 0; otherwise  1; if a transportation link is established between d=c k and market zone l; via mode vk Lvk ¼ kl 0; otherwise  1; if collection center c is the successor of collection center c Ycc ¼ 0; otherwise  1; if collection center c is the successor of dismantler p Ypc ¼ 0; otherwise  1; if dismantler p is the successor of collection center c Ycp ¼ 0; otherwise  1; if collection center c is served by truck t Yct ¼ 0; otherwise

In terms of the sets indices, parameters, and decision variables defined above, the bi-objective, multi-echelon, single product and single period closed loop supply chain design problem can be formulated as follows. 4.2.1. Objective functions The first objective is to maximize the total profit generated in the CLSCN. The profit is to be obtained by subtracting the total cost borne by the company from the income earned in the network. The sources of income in the CLSC network are the customer zones where the finished products are to be sold out, the spare market where the demand of repaired parts occurs, and the savings generated by integrating the recycled material into their manufacturing operations. The mathematical representation of total income generated in the CLSC is:

XXX l

k

vk

vk SPl Xkl þ

XX s

m

RSPm Xsm þ

XX i

r

Revr XDIir

(I)

Various costs borne by the company include the costs of maintaining effective functioning of each facility and the flow in between the facilities. Thus the total cost includes operational costs and transportation costs. Furthermore, the operational costs incurred in the forward chains are due to the purchasing of raw material, the production of components, product assembly and on-time delivery of the products to their customers. The reverse chain requires the company to pay incentives to their customers under a take back scheme for returning EOU products, and the company also bears various costs in the reverse chain such as dismantling cost, repair cost of components, decomposition cost of materials, and disposal cost. The mathematical representation

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K. Garg et al. / Journal of Cleaner Production xxx (2015) 1e18

of operational costs incurred in the network follows in equation (II).

XXXX r

vi



j

X

þ

i

vj Assj Xjk

vj

X

vi Purir Xijr þ

þ

X

c

m

XXX vk

Dis Xc þ

XXX

k

vj

j

vk Opk Xkl

l

Repm XPQm þ

vj Prodmj Xjk bm þ

X

m

r

X j

XX

þ

4.2.2. Constraints Constraints under which we need to optimize the above two objectives are as follows. 4.2.2.1. Flow balancing constraints.

BB Xlc

XX

c

l

Decr XDIir

vi

(II)

vj

þ

TC

p

t

þ

XX c

þ

XX

Dpc Ypc Yct þ

c

c

!

X

þ

Dpc Ycp

p

X

vi

vk

XX

pq

Dcc Ycc Yct

XX

m

pd TCm XPDm þ

m

XX r

i

s

vj

m

Max f1 ¼

l

þ

j

þ

P

H

vj

PP s

vj

Assj Xjk þ

vj

vj

þ

vk

k

PP

m

vj Nvj Ljk

þ

þ

XX s

vk

X

k

H

vk

þ

m

vk

XX

XX

l

þ

X

m

pd TCm XPDm

BB Xlc þ

c

l

p

XX i

r

X vi

Nvi þ

X vj

Nvj þ

X vk

Nvk

@ X

XX vk

c r; i

(2)

vk Xkl

c k

(3)

l

vj

Xjk  Pcapj

c j

(4)

k

XXXX r

vi

j

X

Dis Xc þ

vi Purir Xijr þ

i

Repm XPQm þ

XX c

!

XXX m

X

m

Dpc Ypc Yct þ

c

r

Dcc Ycc Yct þ

XX

c

c

p

j

vj

vj Prodmj Xjk bm

Decr XDIir þ !

Dcp Ycp

þ

X

X

vi

H vi Nvi Lvi ij pq

TCm XPQm

m

TCrdi XDIir

The second objective: In the forward supply chain, trucks are hired by the company from Sigma Logistics. For each facility in the forward chain, Sigma Logistics uses a different set of trucks according to their need. With the help of the second objective we aimed at minimizing the carbon footprint of the company. To control the impact of the extended network on the environment in a cost-effective manner, we minimize the transporting vehicles in the forward direction. Because the company employs a large number of transporting vehicles in the forward chain, this use causes a large amount of greenhouse gases emissions. Hiring more than one truck of lesser capacity is cheaper than hiring a truck with a larger capacity. Thus, in order to fulfill the company's policies towards a cleaner and green environment, the company aims at minimizing the number of vehicles hired in the forward chain. Hence the second objective is as follows:

Min f2 ¼

vj

Xjk ¼

c

XX

TC þ

0 Revr XDIir

XX

t

TCms XQ Sm þ

r

i

vk Opk Xkl þ

Nvk Lvk kl

X

vi Xijr  Rcapir

j

vj

RSPm Xsm

XXX

(1)

Constraint (3) represents that the flow entering each d/c is equal to the flow exiting from the d/c.

TCrdi XDIir

The first objective would be (I)e(II)e(III):

vk SPl Xkl

c r; j

j

XX TCms XQSm

(III)

XXX

vj mm r bm Xjk

k

Constraint (2) shows that the total quantity of each raw material shipped from any supplier cannot exceed the supplier's supplying capacity.

c

TCm XPQm þ

m

vj

i

XX

X X X Hvi Nvi Lvi Hvj Nvj Lvj þ H vk Nvk Lvk ij þ kl jk X

XXX

Constraint (1) represents the quantity of each raw material shipped from suppliers to plant, and it depends on the number of components manufactured there. When the required quantity of raw material is equal to the utilization rate of raw material in the component, the utilization rate of a component in product and the quantity of assembled product shipped from plant to d/c is as follows.

Equation (III) represents the transportation cost which includes the cost of transporting material and products in the forward chain, the cost of shipping returned products from collection centers to the centralized dismantling center, and the cost of shipping components from the dismantler to repair and decomposition centers.

vi

vi Xijr ¼

Constraint (4) ensures that the flow of the product exiting from each plant does not exceed the production capacity of the plant.

XX vk

vk Xkl  Dl

c l

(5)

k

Constraint (5) ensures no shortages of the product at demand point.

X

Xlc ¼ a*Dl

cl

(6)

c

Constraint (6) describes the relationship between the demand and quantity of return products transferred from customers to collection center. The return rate is considered to be dependent on the demand of the product, as a fraction of those purchasing new units would expect to replace their existing units.

Xc ¼

X

Xlc

cc

(7)

l

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K. Garg et al. / Journal of Cleaner Production xxx (2015) 1e18

Constraint (7) calculates the quantity of the returned product at each collection center.

XPQm ¼

X

gm bm Xc

c m

Constraint (8) imposes the flow of each component exiting from dismantler to repairing center. The quantity of the components is calculated by multiplying the repairing rate of mth component with its utilization rate and the quantity consolidated at the dismantling center.

c s; m

(9)

Constraint (9) ensures that the repairing center is not obliged to satisfy the demand of components in the spare market.

XPDm ¼

X

ð1  gm Þbm Xc

c m

(10)

c

Constraint (10) shows that the remaining components of each type at the dismantling center are shipped to the decomposition center for further processing.

X

XDIir ¼

X

hr mm r XPDm

c r

(11)

m

i

Constraint (11) identifies the decomposed quantity of each material exiting from the decomposition center. The quantity is calculated by multiplying the number of units entering the decomposition center with the utilization rate of the material in the component and the recycling ratio of the material.

X vk

vk Xkl  MI*Bkl

Xlc  MI*Alc

c k; l

(12)

c l; c

(13)

Constraint (12) represents that a d/c can only serve the market zone assigned to it. Similarly, Constraint (13) says that a collection center can only collect the returned product from the customer zone assigned to it.

r

j

vi

X Wr  Nvi Capvi

i

k

vj

Xjk  NCapvj Nvj vj

Xjk  WCapvj Nvj

p

vk Xkl

c vj  NCapvk Nvk

vk Xkl  WCapvk Nvk

c vk

XX

c

c

 MDt X

Dcc Ypc Yct þ

XX

c

c

Dcp Ycp

p

c t

(23)

WRc Yct  MWt

c t

(24)

c

XX p

t

p

c

XX XX c

Ypc Yct þ

XX c

Ycc Yct ¼ 1

c c

(25)

t

Ypc Yct ¼ 1

c t

(26)

Ycp Yct ¼ 1

c t

(27)

p

Equation (22) calculates the total weight of returned products collected at the collection center c. Constraints (23) and (24) ensure that maximum tour distance and maximum capacity should not exceed the allotted maximum distance and the capacity of the truck respectively. Constraint (25) implies that every collection center is served and belongs to exactly one tour. Constraints (26) and (27) require every tour to start and end at the dismantler, thus no invalid tour can be constructed. 4.2.2.4. Linking-shipping constraints.

Lvi ij 

X r vj

vi Xijr

c i; j; vi

(28)

c j; k; vj

(29)

c k; l; vk

(30)

Constraints (28)e(30) ensure that there are no links between any two locations through any truck without an actual transportation link in the forward supply chain.

(16) 4.2.2.5. Shipping e linking constraints.

(17)

(18)

r

vi Xijr  MI  Lvi ij

vj Xjk  MI*Lvj jk vk  MI*Lvk Xkl kl

c i; j; vi

(31)

c j; k; vj

(32)

c j; k; vj

(33)

c vk

(19)

c vk

(20)

Constraints (31)e(33) ensure that there is no shipment between any non-linked locations for each type of vehicle in the forward supply chain.

(21)

(34)

k

Nvk  Maxvk

Dpc Ypc Yct þ

(15)

k

XX l

c vj

(22)

vk Lvk kl  Xkl

j

XX

XX

X

Nvj  Maxvj

l

c vj

cc

Xlc W

l

(14)

j

XX k

c vi

X

WRc ¼

vj

ijr

Nvi  Maxvi XX

c vi

4.2.2.3. Constraints related to routing between collection centers and dismantler.

Ljk  Xjk

4.2.2.2. Transportation capacity constraints.

X XX

shows that number of vith trucks required by supplier cannot exceed the number of vith trucks available. Similarly, Constraints (16)e(18) are for plants and Constraints (19)e(21) are for distribution centers.

(8)

c

XQSm  Dm s

9

The second set of constraints are transportation capacity constraints. Constraint (14) represents that the maximum capacity of truck vi used at supplier i should be taken care of. Constraint (15)

vj vk Bkl ; Alc ; Ypc ; Yct ; Ycp ; Ycc ; Lvi ij ; Ljk ; Lkl 2f0; 1gci; j; k; vi; vj; vk; l; c; t; p;

vj

vi vk ; Xjk ; Xkl ; Xlc ; Xc ; XPQm ; XQSm ; XPDm ; XDIir ; Nvi; Nvj ; Nvk Xijr

0

and integer

ci; j; k; vi; vj; vk; r; m; l; c

(35)

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K. Garg et al. / Journal of Cleaner Production xxx (2015) 1e18

X

vk Xkl  MI*Bkl

Constraints (34) and (35) impose the binary, non-negativity and integer restrictions on the corresponding decision variables. The problem (P1) will be the bi-objective mathematical model discussed above.

Xlc  MI*Alc

4.3. Problem (P1)

XXX

vk

r

Max f1 ¼

XXX l

vk

k

þ

PPP vk

þ

P vk

þ

P m

Min f2 ¼

vk SPl Xkl þ

X vi

k

l

XX s

vk Opk Xkl þ

m

XX

X

TC

pd

TCm XPDm þ

XX r

i

X

Nvi þ

vj

Nvj þ

X vk

X

r

0 Revr XDIir

Dis Xc þ

c

XX p

t

XX i

BB Xlc þ

c

l

Hvk Nvk Lvk kl þ

RSPm Xsm þ

c

Dpc Ypc Yct þ !

X

@

vi

vi Xijr

vi

vi Xijr

m

vj

i

XX

c

r

Dcc Ycc Yct þ

c

vj

vj Xjk ¼

XX

j

XX vj

c r; j

(C1)

vi

!

þ

Dpc Ycp

m

j

X

p

vj

Hvi Nvi Lvi ij þ

X vj

pq

XX j

vj

vj

Assj Xjk

vj

Hvj Nvj Ljk

TCm XPQm þ

XX

m

s

TCms XQ Sm

m

(C2)

c vi

(C15)

vj

Xjk  NCapvj Nvj

c vj

(C16)

c vj

(C17)

vj

Xjk  WCapvj Nvj

j

k

vk

vk Xkl

Nvj  Maxvj c k

(C3)

l

XX l

vj

Xjk  Pcapj

c j

(C4)

vk

vk Xkl  Dl

c l

(C5)

k

Xlc ¼ a*Dl

cl

(C6)

c vj

(C18)

vk Xkl  NCapvk Nvk

c vk

(C19)

c vk

(C20)

k

XX

k

XX

vk Xkl  WCapvk Nvk

k

Nvk  Maxvk WRc ¼

X

c

c vk

(C21)

cc

Xlc W

(C22)

l

Xc ¼

X

cc

Xlc

(C7)

l

XPQm ¼

X

gm bm Xc

XQSm  Dm s XPDm ¼

XX p

c m

X

(C9) c m

(C10)

X m

hr mm r XPDm

c r

(C11)

XX c

Dcc Ypc Yct þ

XX

c

c

c t

Dcp Ycp

p

(C23)

WRc Yct  MWt

c t

(C24)

c

XX ð1  gm Þbm Xc

c

XDIir ¼

c

X

c s; m

Dpc Ypc Yct þ

 MDt

(C8)

c

i

X

vj

Prodmj Xjk bm þ

j

k

l

X

i

Nvi  Maxvi

XX

c r; i

(C14)

TCrdi XDIir

k

 Rcapir

c vi

XXX

vi Purir Xijr þ

Decr XDIir þ

XX

c

Nvk

vj mm r bm Xjk

vi Xijr Wr  Nvi Capvi

j

XX

X

j

X

m

XX

(C13)

i

XX XXX

¼

r

vi

(C12)

c l; c

XXXX

Repm XPQm þ

Subject to:

XX

j

c k; l

p

t

XX p

Ypc Yct þ

XX c

Ypc Yct ¼ 1

Ycc Yct ¼ 1

c c

(C25)

t

c t

(C26)

c

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K. Garg et al. / Journal of Cleaner Production xxx (2015) 1e18

XX c

Lvi ij  vj

Ycp Yct ¼ 1

X r

vi Xijr

vj

vk Lvk kl  Xkl

r

(C27)

vj

vi vk Xijr ; Xjk ; Xkl ; Xlc ; Xc ; XPQm ; XQSm ; XPDm ; XDIir ; Nvi; Nvj ; Nvk

0

Ljk  Xjk

X

c t

p

vi Xijr 

c i; j; vi

(C28)

c j; k; vj

(C29)

c k; l; vk

(C30)

MI  Lvi ij

vj  MI*Lvj Xjk jk vk  MI*Lvk Xkl kl

c i; j; vi

(C31)

c j; k; vj

(C32)

c j; k; vj

(C33) vj

vk Bkl ; Alc ; Ypc ; Yct ; Ycp ; Ycc ; Lvi ij ; Ljk ; Lkl 2f0; 1gci; j; k; vi; vj; vk; l; c; t; p;

(C34)

11

and integer

ci; j; k; vi; vj; vk; r; m; l; c

(C35)

The above bi-objective problem (P1) cannot be solved directly using a scalarization approach (Geoffrion, 1968) because each objective is of a different scale. Further, if either the priorities or the relative importance of the objective functions are provided by DM, the problem (P1) can be solved by using a lexicographic approach or a weighted sum approach after normalizing each of the objective functions. Generally, it is not easy to prioritize or to prespecify the relative importance of the objective functions. Rather, DM is interested in knowing the possible trade off between objectives. In such situations, one of the best suited methods/approaches to solve a multi-criterion problem is an interactive multi-criterion decision approach after normalizing the objectives.

4.4. Methodology: interactive multi-objective programming approach algorithm In this section, an approach to solve the above bi-objective mathematical model is proposed.

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K. Garg et al. / Journal of Cleaner Production xxx (2015) 1e18

The flow diagram of the algorithm is presented in Fig. 2. 5. Application of the proposed model to the geyser case study Delhi and its surrounding area, called the National Capital Region (NCR), is one of the biggest metropolitan areas of India. It covers a total area of 33,578 km2 and includes a population of 22,157,000, so Delhi and NCR are highly dense areas [21]. Delhi has a huge market for geysers due to its typical extreme climatic conditions; geysers are a seasonal product in Delhi. Delhi has a cycle of 4 seasons in a year. Geysers help in heating/boiling water for various needs. Customers opt for the products whose replacement or spare parts are easily available as they cannot afford to wait for long periods in case a problem emerges. Therefore, the company wants to implement this model for its operations in Delhi and NCR. The target market for this organization consists of those customers who are not brand conscious and are ready to buy their products. Its major demand is in the category of geysers with capacity of 10, 15, and 25 L. The product life of each respective category is 7e8 years with the highest demand for geysers of 25 L. This particular category of geysers is very popular on account of its cost effectiveness and convenience to install and reinstall in case of a shift in location and low electricity expenditure. The customer segment for 25 L capacity is generally small business houses, medium budget restaurants, and middle class and nuclear families. This organization is running successfully but is not able to target customers who have a high budget and who can purchase 35 L geysers. These categories of customers are well educated and prefer to purchase from brands who practice green and sustainable policies. The company invests a part of their innovation, media, and marketing funds for the creation of user-friendly and environmentally-friendly products. This organization believes in delivering an improved and innovative

product as a part of their continuous product improvement policy, and according to their service policy, every new geyser installed must be served and maintained at company cost. Over the last few years, the company has started to design the geysers in such a way that after acquiring used geysers, their components can be repaired for reuse and from the discarded components, materials can be extracted through recycling. As discussed above, the organization focuses on using these recyclable products in an effective and productive manner to minimize cost. Thus, the organization needs to have a rigorous analysis to extend their supply chain to incorporate value recovery processes. The above CLSC network (Fig. 1) and problem (P1) was proposed for the company to help them to build and deliver concrete and strategic operational cost saving interventions. A numerical example is presented with the help of the data provided by a company's operations manager in order to demonstrate the applicability of the proposed model. The CLSC network depicted by Fig. 1 represents the multiechelon CLSC of the company. The company has two manufacturing units, and each of them has an in-house production facility. Assembling a geyser requires four components, and to produce these components, the manufacturer further needs ten types of raw materials, namely: steel sheet, copper sheet, rod, material for thermostat, nuts and bolts, electric wire, glass wool, plastic sheet for body, indicator bulb, and hardware storage. In the forward direction, there are three suppliers of material: the first suppliers provide steel sheet, glass wool, and plastic sheet for the body; the second supplier provides copper sheet, rod, and material for thermostat; and the third supplier provides nuts and bolts, electric wire, indicator bulb, and hardware storage. The following data is used in the multi-echelon CLSC model for a single product.

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13

Fig. 2. Flow chart of Interactive Multi-Objective Programming Approach Algorithm.

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K. Garg et al. / Journal of Cleaner Production xxx (2015) 1e18

Table 2 Data related to raw material.

Steel sheet Copper sheet Rod Material for thermostat Nuts and bolts Electric wire Glass wool Plastic sheet for body Indicator bulb Hardware storage

Weight (in Kg.)

Recycling ratio

Decomposition cost (Rs. per unit)

Revenue earned (Rs. per unit)

TCrdi

2.5 0.750 0.700 1 0.600 0.500 0.200 0.500 0.200 2

0.75 0.50 0 0.60 0.85 0 0.9 0 0.45 0.55

250 100 0 50 20 0 50 0 150 50

250 100 0 50 20 0 50 0 150 50

650 250 60 240 100 350 200 200 400 300

and NCR. To enhance their image in the market, the company adopted a waste-free policy; they face few economical and environmental issues such as high transportation costs and concerns over their carbon footprint. To overcome their problems, we have proposed the model and a methodology to solve it. While this paper is motivated by the specific company case, the model can be applied to any company involved in the manufacturing of large products that have a modular product structure. The model formulation in Section 4 uses general notations and parameters. Moreover, in India, a number of industries and companies have not yet adopted waste-free policies, so this research can be appropriately applied.

Table 3 Data related to 4 components of geyser. M1 Production cost (in Rs.) j1 100 j2 100 Repairing cost (in Rs.) RSPm 150 300 pq TCm 20 TCms 10 pd TCm 20

M2

M3

M4

200 200

250 250

350 350

250 500 25 15 25

350 700 25 15 25

300 1000 30 20 30

6. Results and managerial implications The company's 25 L geysers are sold at Rs.12000, Rs.12200, Rs.12150, Rs.12230, Rs.12340 and Rs.12500 respectively in the markets of Northeast Delhi, Northwest Delhi, East Delhi, West Delhi, South Delhi, and Central Delhi. Distribution centers need to do some operations such as loading, unloading, and packaging for proper dispersal of the end products to their demanding customers. The per unit operating cost at distribution centers is Rs.270, Rs.290 and Rs.310 respectively. Distribution center 1 is liable to cater to the demand of North-east and Northwest Delhi, Distribution center 2 is liable towards East and West Delhi customer zones, and Distribution center 3 is liable towards South and Central Delhi customer zones. All the activities related to the distribution in the forward chain are carried out by hiring different types of trucks from Sigma Logistics and the data given in Table 4. In the reverse supply chain, the dismantler is responsible for the collection of returned products from collection centers, and the dismantler owns 3 trucks. Each cannot carry more than 500 kg in a trip. Tables 2e5 provide remaining data. Vehicles available at each node are of different capacities. The above data related to trucks shows that the amount of increase in the capacity of trucks and the corresponding increase in hiring cost are not proportional. In fact, the hiring price of a truck increases more because of the environmental friendliness factor (low green house gas emission, less fuel consumption) of the vehicle. The research focus of this paper is motivated by a problem faced by an Indian company with expertise in the manufacturing of electronic and electrical goods. The biggest product manufactured by them is geysers, which are highly demanded products in Delhi

6.1. Results discussion The above data is employed to validate the proposed model. A LINGO code is generated for the mathematical model proposed in Section 4 with the help of data provided by the company. The problem is solved using LINGO11.0 (Thiriez, 2000). First we have optimized the objectives individually. For the given set of data, the optimal cost of the network obtained is Rs. 3,514,824 and the corresponding composite number of vehicles hired is 42. Similarly, when the second objective number of vehicles is optimized, the value obtained is 26 and the corresponding value for cost objective is obtained as Rs. 2,903,045. From the above, it can be concluded that if we want to choose a SCN with a lower number of vehicles, there is a substantial decrease in the revenue as more of the vehicles chosen are of large capacities and therefore have a higher rental fee. Similarly, if we want to choose revenue as an optimal solution, the number of vehicles hired increases because the rental fee for low capacity trucks is low. This clearly shows the conflicting nature of the chosen objectives in a real decision-making situation. A vector of optimal solutions {3514824; 26} defines the ideal solution to this problem. It is to be noted that {3514824; 42} defines the revenue optimum solution and {2903045; 26} defines the optimum solution for the number of trucks hired. Hence, the targets set for objectives 1 and 2 are Rs. 3,514,824 and 26 respectively. A multi-objective program aims at finding the most efficient solutions. All the possible trade-offs between the objectives can be traced through a Pareto optimal curve (Pareto-efficient frontier). A

Table 4 Data related to trucks in the forward direction. Suppliers Type of truck Capacity of trucks (in numbers) Capacity of trucks (in weight) Hiring cost Maximum number of type of vehicles

vi1 30 350 500 10

Plants vi2 50 450 1500 10

vi3 60 600 3000 10

vj1 30 350 500 12

Distribution centers vj2 50 500 1800 12

vj3 60 600 3200 10

vj4 75 750 4500 10

vk1 30 350 500 12

vk2 50 500 1800 12

vk3 60 600 3200 10

vk4 75 750 4500 10

Please cite this article in press as: Garg, K., et al., A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.02.075

K. Garg et al. / Journal of Cleaner Production xxx (2015) 1e18 Table 5 Distance among collection centers, collection centers, and dismantler.

C1 C2 C3 C4 C5 C6

15

Table 6 Solution of iteration 1.

C1

C2

C3

C4

C5

C6

Dismantler

Weighting vector

Normalized criterion vector

Objective function

0 30 40 60 50 33

30 0 20 36 54 43

40 20 0 40 52 39

60 36 40 0 25 50

50 54 52 25 0 18

33 43 39 40 18 0

20 25 30 25 30 28

l1

l2

f1/3464800

f2/26

f1 ðxÞ ðxÞ l1 3464800 þ l2 f226

0.93 0.78 0.5 0.35 0.17

0.07 0.22 0.5 0.65 0.83

0.989631 0.964434 0.89205 0.889013 0.883027

1.346154 1.153846 1 1 1

0.826126 0.498412 0.05397 0.33885 0.67989

Pareto frontier helps the decision maker to read the all possible alternative optimal solutions through a graphical representation. The Pareto optimal curve of the problem (P1) is illustrated in Fig. 3. The Pareto optimal curve of the model exhibits the trade-off between the total profit generated and the number of vehicles hired. Also, we can find that the absolute value of the curve's slope decreases and the curve becomes flatter when the total profit incurred decreases. Such a characterization allows us to determine the trade-offs between the two objectives. Each point in this frontier corresponds to a different optimal CLSCN configuration. The optimal curve of the model in Fig. 3 is obtained by assigning weights in the interval [1, 0] to the first objective and in the interval [0, 1] to the second objective. Among these choices, one can be selected based on the decisionmaker's preferences. We have used an interactive approach to determine the criterion vector values corresponding to a finite number of dispersed weighting vectors in order to find the DM's preferred alternative solution on the Pareto curve. The Interactive Multi-Objective Programming Approach algorithm begins with the generation of weighting vector space (WVS)L1 of 50 vectors in the interval [0, 1] and then we have filtered them to obtain the most distinct vectors. The following five distinct vectors {(0.93,0.07), (0.78,0.22), (0.5,0.5), (0.35, 0.65), (0.17,0.83)} are filtered, then the corresponding normalized criterion vector (NCV) and objective function are obtained after solving weighted sum problem (P4) for these five weighting vectors. Results of this iteration are summarized in Table 6. The three most distinct NCV's obtained after filtering the five NCV's are {(0.989631, 1.346154), (0.964434, 1.153846), (0.89205, 1)}. Values of original criterion vector corresponding to these three most distinct NCV's are presented in Table 7. Original criterion vectors corresponding to these criterion vectors are presented to the decision maker, and the decision maker prefers the objective

values corresponding to vector CV2 (i.e. {3389816, 29}). The weighting vector corresponding to CV2 is identified with l1 ¼ 0.78 and l2 ¼ 0.22. WVS L2 of 50 weighting vectors is generated in the reduced interval, where l1 2 [0.64, 0.85] and l2 2 [0.15, 0.36]. WVS of 50 vectors are filtered to obtain the following five distinct weighting vectors {(0.66, 0.34), (0.71,0.29), (0.76,0.24), (0.81, 0.19), (0.85,0.15)}. The value of the NCV and objective functions are obtained after solving weighted sum problem (P4) for these five weighting vectors. Five weighting vectors and corresponding values of NCV and objective function are summarized in Table 8. The most distinct NCV's obtained after filtering above five NCV's are {(0.938231, 1.076923) and (0.970989, 1.192308)}. The value of original criterion vector corresponding to these two most distinct NCV's are obtained and presented in Table 9. Original criterion vectors are presented to DM. DM prefers the solution corresponding to the CV2 (i.e. {3250784, 28}). The weighting vector corresponding to CV1 is identified with l1 ¼ 0.71 and l2 ¼ 0.29. Now a WVS L3 of 50 weighting vectors is to be generated in the reduced interval, where l1 2 [0.735, 0.785] and l2 2 [0.215, 0.265]. However, the bounds of these intervals are insignificantly apart. It becomes difficult to generate a WVS L3 of unique 50 vectors. Thus it becomes invariant to move to the next iteration. Among the preferred criterion vector in two iterations, the decision maker prefers the solution corresponding to weighting vector (0.78, 0.22). The non dominated values of profit maximization objective and number of vehicles minimization objective corresponding to weighting vector (0.78, 0.22) are 3389816 and 30 respectively. The optimal flows in between the facilities are as shown in Table 12. Table 12 presents the solution in terms of the notation used in Section 4 for decision variables. Fig. 4 presents routing of the company-owned vehicles to collect all returned products from regional collection centers to centralized collection centers.

6.2. Managerial implication To look into the managerial insight of the decision maker's most preferred non dominated solution, we break the revenue earned and cost born by the company for the two integrated chains. With the implication of the proposed CLSC, the company can earn a revenue of Rs. 6,538,006, while incurring a sum of Rs. 3,148,190 in the form of operational and transportation costs. As a result, setting up the proposed CLSC network helps the company to gain Rs. 3,389,816 as profit. In the forward chain of the network, the company has to spend Rs. 2,882,500 for fulfilling the demand, while the company can earn Rs. 6,359,150 by selling finished products. Thus, Table 7 Original criterion vectors.

Fig. 3. Pareto optimal curve of the problem (P1).

f1 f2

CV1

CV2

CV3

3,478,380 35

3,389,816 30

3,135,400 26

Please cite this article in press as: Garg, K., et al., A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.02.075

16

K. Garg et al. / Journal of Cleaner Production xxx (2015) 1e18

Table 8 Iteration 2 solution. l1

l2

f1/3464800

f2/26

f1 ðxÞ ðxÞ l1 3464800 þ l2 f226

0.66 0.71 0.76 0.81 0.85

0.34 0.29 0.24 0.19 0.15

0.93592 0.938231 0.943715 0.970989 0.976387

1.076923 1.076923 1.076923 1.192308 1.192308

0.245038 0.346931 0.458762 0.552655 0.656852

Table 9 Original criterion vectors.

f1 f2

CV2

CV4

3,250,784 29

3,364,284 31

Table 10 Cost break up in both directions. Cost parameters (in Rs)

Forward supply chain

Reverse supply chain

Revenue Cost incurred

6,359,150 2,882,500

178856 265690 Fig. 4. Routing between collection centers and centralized collection centers.

Table 11 Cost break up in both directions (with 10% increased demand). Cost parameters (in Rs)

Forward supply chain

Reverse supply chain

Revenue Cost Incurred

6,982,740 3,176,000

178130 262280

the company could save Rs. 3,476,650 in the forward chain by hiring 30 trucks. The usage of trucks is provided in Table 12. The reverse channel of the network begins with the collection of EOU/EOL geysers from their users following various recovery processes. Implementation of the reverse logistics requires a sum of Rs. 265,690 and can generate Rs. 178,856. A break up of the revenue and cost incurred in the forward and reverse channels is demonstrated in Table 10. The cost parameters in Table 10 divulge that the individual set up of reverse logistics does not prove feasible as the individual set up incurs monetary losses. The integration of a recovery system in the traditional supply chain could lower the company's profit by Rs. 86,834. However, it may help the company to develop a green brand for its product. This might reflect in an increase for the demand of the company's product. The managerial insight of the model results are: (1) the company may be able to attract more customers for their 25 L geyser in the upcoming period, and (2) the added customers would be first time users, so the amount of returned products might not increase in the next period but would likely increase in subsequent years. After

reading the results, a decisionmaker is also interested in knowing the impact of a 10% increase in demand on model behavior as it will help in understanding the practical applicability of the model better. A 10% increase in demand of first time user lowers the return rate by 3%. The break of revenue and cost incurred in the forward and reverse channels of the network with new demand is given in Table 11. Table 11 clearly depicts that the company can attain a profit of Rs. 3,722,591 which helps in gaining Rs. 332,775 more, resulting in a 9.8% increase in profits. However, the reverse channel is not significantly affected by the increase, and it is only impacted by the quantity of the return products. To improve the economical performance of the integrated reverse channel, the company will need to conduct awareness programs among their customers to ensure higher returns. 7. Conclusion Extending a product's life cycle from cradle to cradle through a reverse logistic set-up not only lends a hand in the reduction of waste and hazardous material but also it provides industries with new business opportunities. Modelling a CLSC through the integration of forward and reverse supply chains for a company is an exigent problem. The waste management policy leads to the formulation of a bi-objective mathematical model. In this paper, we investigated the trade-offs between the operational and environmental performance measures of the proposed CLSCN. The highlight of the paper is to

Table 12 Distribution plan (non dominated solution). vi : X vi3 ¼ 231, X vi3 ¼ 231, X vi3 ¼ 231, X vi3 ¼ 289, X vi3 ¼ 289, X vi3 ¼ 289, X vi3 ¼ 231, X vi3 ¼ 231, X vi3 ¼ 231, X vi3 ¼ 289, X vi3 ¼ 289, X vi3 ¼ 289, X vi3 ¼ 231, Xijr 111 117 118 121 127 128 212 213 214 222 223 224 315 vi3 ¼ 231, X vi3 ¼ 231, X vi3 ¼ 231, X vi3 ¼ 289, X vi3 ¼ 289, X vi3 ¼ 289, X316 319 3110 325 326 329 vj vj2 vj4 vj3 vj4 vj1 Xjk :X11 ¼ 42, X11 ¼ 138X13 ¼ 50, X22 ¼ 175, X22 ¼ 115 vk :X vk3 ¼ 100, X vk4 ¼ 80, X vk2 ¼ 35, X vk3 ¼ 50, X vk2 ¼ 90, X vk4 ¼ 80, X vk4 ¼ Xkl 11 12 23 23 24 35 36

vi3 ¼ 289 X3210

85

Xlc :X11 ¼ 30; X22 ¼ 24; X33 ¼ 25; X44 ¼ 27; X55 ¼ 24; X66 ¼ 25 Xc : Xc1 ¼ 30, Xc2 ¼ 24, Xc3 ¼ 25, Xc4 ¼ 27Xc5 ¼ 24, Xc6 ¼ 25 m :X m1 ¼ 23, X m2 ¼ 31, X m3 ¼ 24, X m4 ¼ 23 Xpq pq pq pq pq m :X 1 ¼ 60, X 2 ¼ 48, X 3 ¼ 50, X 4 ¼ 40 Xqs qs qs qs qs m :X m1 ¼ 133, X m2 ¼ 125, X m3 ¼ 133, X m4 ¼ 133 Xpd pd pd pd pd

Nvi : Nvi1 ¼ 0, Nvi2 ¼ 4, Nvi3 ¼ 5 Nvj :Nvj1 ¼ 4, Nvj2 ¼ 1, Nvj3 ¼ 1, Nvj4 ¼ 5 Nvk :Nvk1 ¼ 0, Nvk2 ¼ 3, Nvk3 ¼ 3, Nvk4 ¼ 4

Please cite this article in press as: Garg, K., et al., A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.02.075

K. Garg et al. / Journal of Cleaner Production xxx (2015) 1e18

manage environmental issues such as the increasing usage of transporting vehicles and the resulting carbon emissions from them while extending the existing supply chain network. The proposed model is successful in designing integrated forward and reverse logistics networks while considering the strategies at both strategic and tactical levels. The inflow of returns with better recovery options substantially influences the economic benefit for business by increasing the demand for new products in first customer markets. It is thus suggested that there should be sufficient managerial efforts and initiatives to create awareness among the customers for better marketing of remanufactured products. Many research directions still require intensive research. Uncertainty is one of the important problems in supply chain management. It is worthwhile to take into account uncertainty of parameters such as demand and return. The model that we introduced can be extended by considering product remanufacturing, operational activities such as inventory, choice of technology, and mode of transport. Determining the price of remanufactured parts e which is a function of other factors such as demand, manufacturing process, and environmental concerns, particularly for products that have a short life cycle e can be a subject of future research. This proposed model is designed for a single period, but it can be extended to consider multiple periods. APPENDIX A Let us define a multi-objective programming problem as follows (Steuer, 1986):

3 f1 ðxÞ 7 6: 7 VMaxFðxÞ ¼ 6 5 4: fk ðxÞ subject to X2S 2

(AP1)

Geoffrian Scalar equivalent problem can be formulated for P  l(fixed), l2L ¼ fl2Rk  ki¼1 li ¼ 1; li  0g as follows:

Min s:t x2S

k P

li fi ðxÞ

i¼1

(AP2)

Definition 1 (Geoffrion, 1968): Efficient Solution-x0 is said to be efficient solution of (AP1) if x02S and there exists no other feasible point x such that F(x)  F(x0) and F(x)s F(x0). Definition 2 (Geoffrion, 1968): Proper Efficient Solution-x0 is said to be a properly efficient solution of (AP1) if it is efficient and if there exists a scalar M > 0 such that, for each i, we have

  fi ðxÞ  fi x0   M fj x0  fj ðxÞ For some j such that fj ðxÞ < fj ðx0 Þ whenever xXX andfi ðxÞ > fi ðx0 Þ. Lemma, (Geoffrion, 1968): Optimal solution of the problem (AP2) is properly optimal solution of the problem (AP1).

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Please cite this article in press as: Garg, K., et al., A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.02.075