Journal of Cleaner Production 249 (2020) 119348 Contents lists available at ScienceDirect Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro A multiobjective optimization model for sustainable reverse logistics in Indian E-commerce market Pankaj Dutta a, *, Anurag Mishra a, Sachin Khandelwal b, Ibrahim Katthawala b a b Shailesh J. Mehta School of Management, Indian Institute of Technology, Bombay, Powai, Mumbai, 400076, India Flipkart Internet Pvt. Ltd., Embassy Tech Village, Devarabeesanahalli, Bellandur, Bengaluru, 560103, India a r t i c l e i n f o a b s t r a c t Article history: Received 16 February 2019 Received in revised form 14 November 2019 Accepted 16 November 2019 Available online 18 November 2019 A cleaner and sustainable environment is becoming a topmost priority for both owners and stakeholders involved in businesses. It could be achieved by adopting better sustainable practices like reduction in waste through process of recycling, recovery and remanufacturing which helps to minimize both the cost and environmental losses. With a recent surge in E-commerce market and online shopping in India there is a need for a more efficient, sustainable and reliable reverse logistics design by including cost, environmental and social factors into consideration. With the factors considered above this paper proposes a multi-objective logistics network model for the return products specifically pertaining to the Indian Ecommerce market. The components of this multi-echelon supply chain considered are Customer Markets, Warehouses, Delivery Hubs, Landfills, Incineration Centres and Recycling Centres. The multiobjective optimization is done on the three fronts of sustainability, namely, economical, represented by cost, environmental, represented by environmental impact of different process, and social, which is represented by work days created and lost due to harms at work. Different technologies are considered in delivery hubs and mother warehouse which could result in a more efficient way of transferring and processing products. Weighted goal programming (WGP) technique is used by weighing different objectives to minimize cost, environment impact and maximize the social responsibility. Finally, - model is validated with a numerical example based on an online retail selling clothes. This study will help the managers in deciding the number of facility stores and warehouses needed to open and operate, technology to be adopted for a more efficient way of transferring and processing products. © 2019 Elsevier Ltd. All rights reserved. Handling Editor: Kathleen Aviso Keywords: E-commerce Reverse logistics Sustainability Multi-objective India 1. Introduction In the modern India, the value and importance of E-commerce is growing day by the day as increasingly, consumers are starting to prefer shopping from the comfort of their homes. The growth is further fuelled by the large selection, transparency of prices across retailers, availability of discounts, and rising internet penetration across the Indian populace. The Indian e-commerce market is US$ 50 Billion in size and expected to reach US$ 64 Billion by 2020 (Ecommerce sectoral report, 2018). An important part of any Supply Chain (SC) is its Reverse Logistics(RL). Reverse Logistics is “the process of planning, * Corresponding author. E-mail addresses: pdutta@iitb.ac.in (P. Dutta), anuragkrmishra22@gmail.com (A. Mishra), sachin.k@flipkart.com (S. Khandelwal), Ibrahim.k@flipkart.com (I. Katthawala). https://doi.org/10.1016/j.jclepro.2019.119348 0959-6526/© 2019 Elsevier Ltd. All rights reserved. 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 recapturing value or proper disposal. Remanufacturing and refurbishing activities also may be included in the definition of reverse logistics” (Hawks, 2006). When it comes to e-commerce, the additional emphasis is on returns management, a component of RL. According to Rogers et al. “Returns management is defined as the process by which activities associated with returns, gatekeeping, and avoidance that are managed within the firm and across key members of the supply chain” (Rogers et al., 2002). Returns management if handled efficiently and smoothly could make the difference in firms, addi€llecke tionally it also helps to maintain the customer loyalty (Ro et al., 2018; Ramanathan, 2011). The Worrisome thing is that still RL or returns management is not being treated seriously by firms and instead taken as a burden and unimportant for their growth (Rogers et al., 2013). 2 P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348 Another important objective that is looming over SC managers is the issue of sustainability, firms are now being held responsible for the environmental impact and social performance of the ecosystem they create. The pressure is not only derived from the government rules and regulations, but also from various stakeholders, employees, customers and NGOs. The 3 main elements of a sustainable supply chain according to the Triple Bottom Line Approach are (Nikolaou et al., 2013) e 1. Economic e This relates to the profitability of the supply chain. The economic concept has been there before the concept of sustainability and this is what most firms have been focusing on. Profitability in the context of supply chain is essentially the reduction of costs. 2. Environmental e The impact of all the different processes and activities on the environment. It is certainly difficult to measure the environment impact and less research has been done on this front. 3. Social e The companies should not only restrict themselves to seeing economic and environmental benefits but should push themselves to achieve social benefits. This implies that companies, besides making profit, must think about compliance with legal necessities, ethical principles, and esteem for people and communities in all their activities (Pai et al., 2016). Very few research has been carried out applicable to the application of sustainability in E-commerce. Most research has only focused on exploring the sustainable supply chain scenario and strategy. The uniqueness of this paper lies that it applies the concept of Sustainable Supply Chain Management to RL, and especially to returns management in the context of Indian E-commerce scenario. This paper has used the triple bottom line approach and considered the economic, environmental and social factors. Fig. 1 describes the framework of the paper. The remainder of this paper is organized as follows. In Section 2, the literature in this area of research is reviewed. In Section 3, the model related to the RL in E-commerce companies is formulated. Section 4 deals with the solving methodology. Section 5 presents a numerical example based on online retailer for cotton based clothing. Section 6 includes the results obtained from solving the numerical example using the WGP optimization method. Finally, Section 7 presents the managerial implications and conclusion. 2. Literature review This paper relates to the literature that considered RL-SC in single channel structure and examines the location of delivery centres, mother warehoused and recycling centres. It also considers the technologies used in the various hubs and warehouses which could help in increasing efficiency. We attempt to propose a model for a RL network design problem, by considering the green and sustainability issues and use a multi-objective Mixed Integer Linear Programming (MILP) with goal programming to solve the model. Henceforth, the literature survey in this study is subdivided into three sections: (1) RL for supply chain (SC); (2) RL in E-Commerce SC; (3) Sustainability. 2.1. Modelling of RL in supply chain In one of the first study carried out by Fleischmann et al. (1997) studied various characteristics and thereby suggest improvements in RL system. The study categorised the dimension of RL system into four major parts: Reuse motivation, type of recovered items, form of reuse and involved actors. Also elaborated different models of RL that exist, elaborated the distribution management and inventory Fig. 1. Framework of the paper. management. Pishvaee et al. (2009) developed an integrated forward and reverse logistics network design. They formulated a MILP with deterministic values and then a stochastic equivalent of the model is developed using the scenario-based stochastic approach. Lee and Chan (2009) aimed to find a relevant model for optimization of products that offered an RL network based on radiofrequency identification (RFID) technology. Das and Chowdhury (2012) aimed to minimize the overall processing costs and they suggested a recycling, logistics model for various electronic product wastes. They had broken the recycling phase into four major phases namely collection, separation, recycling and repair. Diabat et al. (2013) had designed a multi-echelon Rl design to minimize the total cost consisting of renting, inventory, carrying cost, material handling and shipping cost. Further, Genetic algorithm and Artificial immune system was implemented to run the model and compare the results. Godichaud and Amodeo (2015) had integrated the process of return in the conventional SC and proposed ways in which remanufacturing could be a profitable option by implementing a well-sought inventory control policy. The model deals with the stochastic demands and stochastic returns. This model is further tested on three inventory policies corresponding to different decision making. Alshamsi and Diabat (2015) proposed a generic reverse logistics design where they used a MILP) to optimize a RL network, which P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348 decides on the number of sites to open, the capacities of the inspection centres and remanufacturing facilities. Galvez et al. (2015) designed a RL network for a biogas plant. Network characterization was done by defining different processes involved, actors, variables associated are identified followed by describing different scenarios possible and where the equipment could be located, additionally defining the logistics network route and translating into mathematical language using linear equations and then Analytic Hierarchy Process (AHP) was used to make a peer comparison. Kumar et al. (2017) have proposed a multi-period, multi-echelon closed loop supply chain model in there model they also address the issue of vehicle routing in their model. The network flow was such there were facility stores, distributors to receive goods directly form the customer or the facility store, used goods directly collected from the customer and distributed to the disassembly location then gods for disposal are sent to the location of disposal and repaired to the distribution center from where they could again be distributed and recycled goods again to the facility location and from here they again enter the supply chain. The products that were received from the remanufacturing center and repairing center were sold to the secondary markets. Then solved the case using Artificial immune system and particle swarm optimization and compared the results from both the algorithms. Bortolini et al. (2018) designed a biobjective fresh food supply chain networks by considering both reusable and disposing packaging containers, they applied this model in an industrial application in Italy and suggested a shipping mix of both containers are required. 2.2. RL in E-Commerce SC E-commerce is defined as sharing business information, maintaining business relationships, operating business negotiations, settling and executing agreements by means of telecommunication networks, often the Internet, to achieve business transactions. Kokkinaki et al. (2000) talks about the relation between RL activities and the information technology to examine these in the field of E-Commerce applications. Dekker et al. (2002) through their panel discussion related to RL in E-Commerce focussed on issues pertaining E-commerce and RL, ranging from operations for collection, selection and decision making for the optimal recovery option of post-retail or surplus products nearing their life cycle. Yan et al. (2012) have designed a reverse network design for e-commerce by considering four participants: factories, online retailers, the 3 PLs and markets and only one type of the product for the Ecommerce enterprise and used a MILP to minimize the logistical cost of the whole system. Guo et al. (2017a,b) have proposed a multi-period model for the reverse network design of the apparel E-commerce enterprises and have eight elements in their model ranging from customers to secondary markets with an aim to minimize the total logistics cost as well as the number of primary recycling and union recycling centre. The model was validated on a shanghai apparel ecommerce enterprises. Guo et al. (2017a,b) had designed a Closed loop supply chain (CLSC) network with an aim to reduce the carbon emissions for fresh food e-commerce enterprises with route planning the model had dual objectives one to reduce the overall cost of the system and the other is to reduce the routing cost. The model was validated on a shanghai commerce enterprise. 2.3. Sustainability and multiple goals in a RL network The Integration of the green concepts and sustainability is an evolving area of research. Along with sustainability the modelling of reverse logistics network design with multiple objectives is gaining interest of the researchers. The optimization in cost 3 function of model when coupled with additional goals like social, customer satisfaction, enhancing service performance helps to have a more robust and sustainable network design. Chaabane et al. (2012) proposed a model for sustainable supply chain with multiple goals of minimizing the costs and also reducing the negative effects on the environment. Paksoy et al. (2010) suggested a multi objective model for a CLSC. The model aims to minimize the raw material and transportation cost along with minimizing the CO2 emissions. Ramezani et al. (2013) developed a stochastic multi objective model considering three echelons in forward direction and two echelons in reverse supply chain. The paper aimed at maximizing the profit, customer responsiveness and quality. Silva et al. (2013) had developed a return-oriented packaging model to optimize the waste generation and reduce the environmental impacts keeping in mind the minimization the costs and use of resources. Nikolaou et al. (2013) had developed a model which included both the social responsibility of companies and sustainability in the RL SC system. Bing et al. (2014) designed a sustainable network for the household plastic waste, here the authors addressed that efficiency is not the only concern for recycling system, sustainability is also a crucial issue. The paper dealt with plastic recycling in Netherlands, where different scenarios were seen concerning the placement of different facilities pertaining to separation, sorting and inspection and compared with the base scenario. MILP was used to minimize the transportation cost and environmental impact in the network. Ramos et al. (2014) solved a multi-objective vehicle routing problem considering the sustainable aspect i.e. economic, environmental, and social aspects in designing the RL systems. Godichaud and Amodeo (2015) tried to optimize inventory level and service performance. Two types of inventory returned inventory and serviceable inventory were taken into consideration. Three policies were presented with returned products and then simulation was carried on the first objective to optimize the cost function. The second objective was to optimize the fill rate i.e. the fraction of demands that could be immediately satisfied from hand. Govindan et al. (2016) had developed a fuzzy multi-echelon, multiperiod, multi-objective model for a reverse logistics network design. The multiple goals considered are minimize the net present cost, minimize the environmental impacts and optimizing the social responsibility. The model was further validated in a medical syringe industry. Zohal and soleimani (2016) proposed a multiobjective model in a closed green supply chain network which had a seven-layer network with four echelons in the forward supply chain and three echelons in the reverse supply chain. The multiple objectives were to maximize the income and minimize the CO2 emissions and cost. Gold industry real case study problem was formulated and solved using Ant colony optimization. Bal and Satoglu (2018) proposed a reverse network design for Waste electric and electronic equipment (WEEE) with four goals which are minimization of cost, minimization of environmental effect, workforce balance and managing the legal targets. The model is validated on WEEE of home appliances using goal programming. Zarbakhshnia et al. (2019) have suggested a multiperiod, multi objective model for a green closed supply chain where the first objective is to minimize the operational cost, second objective was to minimize the CO2 emissions and third objective was to optimize the number of machines used and then model was validated in a home appliance industry. The main contributions differentiating our efforts from published papers on the subject are as follows. Most of the above cited paper deal with the network design and ignore the integration of both green and sustainable (such as social issues). They have focused on the pure green and 4 P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348 environmental issues. Therefore, it was considered to incorporate social issues besides economic and environmental aspects into a model of decision making. In all the studies, the economic objective is cost minimization considering only the transportation aspect. Nevertheless, have defined our economic objective function considering the transportation and the processing costs as well. Different technologies in delivery hubs and mother warehouses was considered which could result in a more efficient way of transferring and processing products. On the whole, this is the first kind of work that proposed a multi-objective optimization model for sustainable reverse logistics network design in E-commerce market. 3. Model formulation In this research the attempt was to develop a model like that of actual Indian large e-commerce firms. The importance of application of this model increases if considered the fact that such firms maybe undergoing certain changes after the introduction of GST. 3.1. Motivation The inspiration for our model from the way actual E-commerce enterprises are operating in the present scenario, with regards to the warehouse model, wherein the stock is purchased and kept in retailer owned warehouses. The levels/echelons in the actual scenario are a step or 2 higher than in our model, but have taken 2 levels of warehouses, where warehouses will be owned by the firm but the logistics be carried out by a third party. The idea having only two levels in warehouse is not to further make the model complex and also to ease on the mathematical formulation. since the warehouse is just a part of our multi-echelon network design and our main focus is here to optimize the whole network. The model is illustrated in Fig. 2. The components of this multi-echelon supply chain are Customer Markets, Fulfilment centres (Warehouses owned by retailers), and Delivery Hubs, Disposal Centres (Landfills, Incineration Centres and Recycling Centres are the three mediums for disposal in this network). There are only 3 disposal options for the product, namely, recycling, incineration, and throwing in landfill The warehouses will be owned and operated by the firm while the logistics will be provided by third party The distance between the customer market and delivery hub is specified to be less than a certain distance. The costs elements of the reverse logistics include and is limited to transportation, processing at warehouse and fixed cost of establishment of warehouse. 3.3. Model description The forward logistics starts from the fulfilment centre and then RL starts with returns management, when a customer requests their order to be returned for reasons like product is defective or want a size change for the item. Most products are disposed using any of the three options i.e. Incineration, landfill and recycling Centres. Technology in Warehouses: The warehouses and delivery hubs operated by different players often have different way of functioning owing to the different technologies used in them. Some of them maybe highly automated, some partly automated. Objective of the work: In designing the RL network, our objective will be to optimize the three essentials for sustainability; 1. Minimizing the total costs incurred in the network 2. Minimizing the total environmental impact due to transportation and the different types of operations and recycling processes e This impact will be quantified using the Eco Indicator 99 methodology, which is based on the Life Cycle Assessment (LCA) methodology 3. Maximization of Social responsibility e To quantify this, the no. of jobs created, and no. of work days lost due to harms at work will be considered (Pishvaee and Razmi, 2012). The indexes of the different parameters used, the decision variable, the objective functions, and constraints are explained below. 3.4. Mathematical formulation 3.2. Assumptions Index The list of assumptions considered while formulating the model are. f Index of incineration centres f ¼ 1; 2; 3…………f h Index of Delivery Hubs h ¼ 1; 2; 3…………H l Index of landfills l ¼ 1; 2; 3…………L m Index of customer markets m ¼ 1; 2; 3…………M n Index of recycling centres (NGOs) n ¼ 1,2,3 … … … … …. N th Index of technology at delivery hubs th ¼ 1; 2; 3…………Th tm Index of technology at FC tm ¼ 1; 2; 3…………Tm w Index of Fulfilment Centres (FC) w ¼ 1; 2; 3…………W The demand, overall, as well as for each customer market, is deterministic The sellers to the retailer have their own pre-established facilities and they supply to the online retailer using their own logistics There are only 2 levels of warehousing Binary Decision Variables Y th h If delivery hub ‘h’ with technology th is to be opened or not Yf If incineration centre ‘n’ is to be selected for disposal or not Yl If Landfill ‘l’ is to be selected for disposal or not Yn If RC ‘n’ is to be selected for disposal or not Y tw w If FC ‘w’ with technology tw is to be opened or not Parameters Fig. 2. E-Commerce reverse logistics model structure. am Demand from customer market’ bm Returns from customer market ‘m’ bmh Returns from customer market ‘m’ to delivery hub ‘h’ P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348 ghl Amount of returns sent to landfill ‘l’ from delivery hub ‘h’ shn Amount of returns sent to recycling centre ‘n’ from delivery from the Delivery Hubs to either the disposal centres or the FC. To calculate the environmental impact, the Eco-Indicator 99 methodology has been used. This is a damage-oriented method for Life Cycle Assessment which is much shorter than the original LCA methodology. It was commissioned by the Ministry of Housing, Spatial Planning, and Environment, Netherlands. hub ‘h’ mhf Amount of returns sent to incineration centre ‘f’ from delivery hub ‘h’ jhw Amount of returns sent to FC ‘w’ from delivery hub ‘h’ X X X m þ h h X X X tw X X tl mh eith eipw þ eihw eipl þ eihl bmh þ jhw þ ph þ eic c c ghl th X X n 5 eitlpl þ eihn c h shn þ w X X h tw eitlpl þ eihf c l tl (1) shf f Ath h Lost days due to harms at work at delivery hub ‘h’ using technology ‘th’ Atw w Lost days due to harms at work at FC ‘w’ using technology ‘tw’ C Transportation cost of item per km Dhl Distance of delivery hub ‘h’ from landfill ‘l’ Dhn Aggregate distance of delivery hub ‘h’ from organization cluster ‘n’ for recycling Dhf Distance of delivery hub ‘h’ from incineration centre ’f’ Dlw Distance of delivery hub ‘h’ from FC ‘w’ Dmh Distance of market ‘m’ from delivery hub ‘h’ eimh Environmental impact of transporting product from c customer market ‘m’ to delivery hub ‘h’ eihw Environmental impact of transporting product from dec livery hub ‘h’ to FC eihl c Environmental impact of transporting product delivery hub ‘h’ to landfill ‘l’ eihn c Environmental impact of transporting product delivery hub ‘h’ to recycling centre ‘n’ eihf c Environmental impact of transporting product delivery hub ‘h’ to incineration centre ‘f’ eitw pw Environmental impact of processing item at FC ‘w’ with technology tw eith ph Environmental impact of processing item at delivery hub ‘h’ with technology th eipl Environmental impact of processing item at landfill ‘l’ eipn Environmental impact of processing item at recycling centre ‘n’ eipf Environmental impact of processing item at incineration centre ’f’ F th h Fixed cost of establishing delivery hub ‘h’ with technology ‘th’ F tw w Fixed cost of establishing FC ‘w’ with technology ‘tw’ J th h Job opportunity created at delivery hub ‘h’ using technology ‘th’ J tw w Job opportunity created at FC ‘w’ using technology ‘tw’ Oth h Capacity at delivery hub ‘h’ with technology ‘th’ Otw w Capacity at FC ‘w’ with technology ‘tw’ P th h Processing cost at delivery hub ‘h’ with technology ‘th’ P tw w Processing cost at FC ‘w’ with technology ‘tw’ 3.4.1. Objective functions The proposed model has three objective functions i.e. Economic, environmental, and social. 3.4.1.2. Social objective function. To look into the social aspects, given two indices are considered for social responsibility:(1) the number of created job positions; (2) the average lost days resulted from harms at work. X X h X X th tw th tw J th J tw w Aw Y w h Ah Y h þ þ w th (2) tw 3.4.1.3. Economical objective function. This function simply sums up all the costs related to the reverse logistics supply chain. The objective will be to minimize the costs. X X h X X th tw F th F tw w Yw h Yh þ th þ X X X m h th h w tw w tw C Dmh þ P th h bmh X X X XX þ ðCDhl þ Pl Þghl C Dhw þ P tw w jhw þ þ XX h ðC Dhn þ Pn Þshn þ n h X X h l C Dhf þ Pf mhf (3) f Subject to Constraints: tm Y th h ; Y m ; Yl ; Yn ; Yf ε f0; 1g c h; l; w; n; f (4) bmh Oth h c m; h; th (5) jhw Otw w c h; w; tw (6) am bmh c m; h (7) bmh ¼ mhf þ jhw þ shn þ ghl (8) X bmh ¼ bm c m; h (9) h X Y th h 1 c h; th (10) th X Y tw w 1 c w; tw (11) tw 3.4.1.1. Environmental objective function. The environmental objective function is defined based on: -The environmental impact due to transportation from the customer markets to Delivery Hubs, bmh ; mhf ; jhw ; shn ; ghl 2I (12) 6 P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348 According to the equation (1) minimizes the environment impact due to transportation from market to DH, DH to FC, DH to landfill, DH to RC, DH to IC along with the impact caused due to processing at DH, FC, landfill, RC and IC. (2) optimizes the social impact due to establishment of DH and FC. (3) Minimizes the economic cost which includes fixed cost of setting up DH and MW, transportation and processing cost at DH, MW, RC, IC, RC and landfills. (4) ensures the binary constraint for decision variables. (5)e(6) are the capacity constraints for DH and FC. (7) makes sure that demand is greater than the returns. (8) maintains the volume flow constraint where the total returns received is either sent to the disposal centres or FCs. (9) maintains the volume flow constraint for the demand. (10)e(11) maintains the technology constraint that only one technology be selected for DH and MW. (12) enforces that the demand and return be integer values. 4. Methodology To solve the proposed multi-objective model, a WGP optimization method is used. Goal Programming (GP) is tool for multicriteria decision making problem, which can be thought of as being an extension to linear programming to handle objectives that generally opposing to each other. It was first introduced by Charnes et al. in (1955), more explicitly defined by the same authors in 1961 (Charnes and Cooper, 1962) and further developed by Ijiri (1965) during the 1960’s. Lee (1972) introduced the lexicographic goal programming variant where the priority level was added, it is also termed as ‘pre-emptive’ goal programming. Flavell (1976) introduced Chebyshev goal programming where maximum deviation from any goal was calculated. Charnes and cooper (1977) stated the weighted goal programming method. Schniederjans and Hoffman (1992) introduced the 0e1 goal programming. Further, Romero (2001) extended the lexicographic goal programming. Though there are several MODM techniques like goal attainment (Gembicki &Haimes, 1975), Parametric Lp (Adler and Monterio, 1992), Lp metric (Lee, 1980), fuzzy method (Tiwari et al., 1987), in this paper we have used WGP to solve the proposed model. WGP helps the decision maker whenever information relating to trade-offs between different objectives is needed. It also helps to make direct comparisons between objectives. (Tamiz et al., 1998). Larbani and Aouni (2011) in their GP model introduced a new approach for generating efficient solutions. Sen and Nandi (2012) have applied the GP in the field of planation management. Silva et al. (2013) has applied multi-choice mixed integer goal programming for the sugar and ethanol milling company. Huang et al. (2018) developed a GP based model system to study the dynamic analysis of community energy flow. Bal and Satoglu (2018) have used a GP in their triple bottom line approach model for the WEEE items. In the model deterministic values are assumed and a WGP optimization method is applied by weighing all the three objectives of our model iteratively and then finding a suitable combination fulfilling the set goals. Algebraically, a WGP has the following structure: k P Minimize a ¼ ðai ni þ bi pi Þ, (13) where ai and bi are the i¼1 weights for the deviational variables subject to fi(x)þni-Pi ¼ bi, i ¼ 1. . .. m, (14) To deal with the three objectives in our example problem via GP, we need to introduce additional variables. These variables will handle the deviations from the “goal” for each objective. Setting a numeric goal for each objective is up to the user. Then the new equation linking our objective function (Z) and its goals with the new variables will be Zi - bþ i þ bi ¼ Gi i¼1,2,3.. (15) Where i is the no. of goals for that objective function. Zi here describes the function f(x) that we are looking to minimize or maximize, Gi is the target value for the objective, bþ i represents the positive deviation from the target value and b-i is the negative deviation from the target value. For our 3 objectives we will have 3 such equations Environmental: Z1i - bþ 1i þ b1i ¼ G1i (16) Social: Z2i - bþ 2i þ b2i ¼ G2i (17) Economical: Z3i - bþ 3i þ b3i ¼ G3i (18) The new combined objective function would require both the bþ, b terms as our functions are both minimization and maximization functions. There are 2 ways to generate a single objective function from these equations þ þ Non-weighted: Min Z0 ¼ bþ 1i þ b2i þ b3i (19) þ þ Weighted Approach: Min Z0 ¼ q1bþ 1i þ q2b2i þ q3b3i (20) Where q1, q2, q3 are the weights assigned to objectives Z1, Z2, and Z3 respectively. 5. Numerical example To validate the model, the proposed datasets are hypothesized. The data within these has been gathered from formal and informal surveys, meeting, and telephonic conversations with industry experts and some essential data has been taken from a Mumbai based e-commerce company. The scales, sizes, and all approximations used have been inspired from literature as well as experts from the online retail industry. Detailed investigation has been done and experience been gathered to formulate information for testing the model. Below is a proposed hypothetical case study based on online retailer for cotton based clothing (see Table 1). 5.1. Background An online retailer of clothing items is considered. Since each type of textile clothing has different waste disposal and recycling procedure therefore for simplicity purposes, have taken a retailer that sells all kinds of clothing item made of only cotton. While using eco indicators it was important to be careful as each indicator signifies different waste processing methods. Hence, choosing cotton will help to take a uniform single environment impact value vide the Eco-Indicator 99 set of values. The retailer serves in four major markets as listed in Table 2. After outsourcing all the logistics for so long, the firm has finally decided to invest in their own logistics in terms of warehouses to make way for reduced costs and increasing volume of business. They will still be using third party logistics providers for their transportation needs. They plan to set up optimum delivery hubs nearest to any of these four markets. All these cities have their own disposal centres in the form a major landfill or several Non-governmental organizations(NGOs) that are spread throughout the city. They also must set up two fulfilment centres with much larger capacities, that will serve these Delivery Hubs. An analysis of their materials flow has suggested that it would beneficial that they set up these fulfilment centres in any two out of the four cities given in Table 2. P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348 7 Table 1 Summary of literature review. Researchers Reverse Logistics Sustainability Economic Fleischmann et al. (1997) Lee and Chan (2009) Millet (2011) Das and Chowdhury (2012) Huang et al. (2018) Das and Chowdhury (2012) Chaabane et al. (2012) Pishvaee and Razmi (2012) Nikolaou et al. (2013) Ardeshirilajimi and Azadivar (2015) Mohanty and Prakash (2014) Ramos et al. (2014) Roghanian and Pazhoheshfar (2014) Guo et al. (2017a,b) Govindan et al. (2016) Bal and Satoglu (2018) Zarbakhshnia et al. (2019) Our Paper ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Social Objective Single Multiple ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Table 2 Index of customer markets, fulfilment centres, and technology. Index Customer Markets Index Fulfilment Center Index Technology 1 2 3 4 1 A 1 2 B 2 W X Y Z E-commerce Environmental Traditional NonAutomated High Level of Automation ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ The warehouses that they have to setup can either be labour operated or involve considerable extend of automation. In deciding the two options, there is a trade-off in establishing fixed costs versus operating costs. Also, if they decide to establish a traditional warehouse, they would be employing more personnel, thus creating more jobs/work-days, on the other hand the number of harms to individuals in such a setting would be more causing loss in work days. Alternatively, an automated warehouse would employ less people but there would be less harms due to work in this scenario. Fig. 3 represents how the actual problem would be represented on a physical map. The Indexes and locations for Delivery Hubs, Landfills, and Recycling centres are same as that of Customer Markets. 5.2. Fixed cost derivation The data about fixed costs is represented in Table 3. The distance between delivery hubs and fulfilment centres is presented in Table 4. The report was also able to provide with the aggregate distance of the warehouse from the city centres, or the customer markets, in the mentioned case. Same is mentioned in Table 5. The distance between delivery hubs and landfill is presented in Table 6 and the distance between delivery hubs and recycling centres is presented in Table 7. 5.3. Volume and distances The analysis here is being executed for a single period, i.e. one month. The complete demand for a month is appx. 10 million units. The returns that the firm must manage from the above markets is to the tune of 1 mil. (industry average of ~10%). As per past data, majority of the returns, i.e. ~60% are due to a change in the size of the clothing item. These items are simply sent back to the fulfilment centre from the delivery Hub. The remaining 40% of the items are defective and cannot be resold. 5.4. Processing and transportation costs Fig. 3. Illustration of options for Delivery Hubs/Fulfilment Centre. The wage is taken to be an approximate of 20,000 for each worker, and the unit cost of electricity is taken 10 Rs, which is close to the commercial cost of one unit of electricity, although it varies 8 P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348 Table 3 Fixed Cost of establishing Technologies(Rs). Delivery Hubs and Fulfilment Centres with Delivery Hub options Fixed Cost with Tech1 Fixed Cost Tech2 W X Y Z 17340000 17100000 16300000 16900000 22340000 22100000 21300000 21900000 Fulfilment Centre Options Fixed Cost with Tech1 Fixed Cost Tech2 A B 43350000 36375000 55850000 48875000 by states and cities. The units consumed are taken in proportion to the volume handled by each hub. Note that there will be two processing costs for each technology that is employed, that is because the electricity consumed in case of automated warehouse will be higher leading to higher processing cost per unit. The various potential processing at the delivery hubs and warehouses are given in Table 8. Although, the cost should vary with geographies due to differing transporter services as well different diesel rates, it comes out to be roughly around Rs. 1. Per km per unit, and thus, we will take the same for simplicity of calculations. 5.5. Metrics for social and environmental objective Table 4 Distance between delivery hub options and fulfilment Centres(km). FULFILMENT CENTRES DELIVERY HUBS W X Y Z A B 20 1400 1450 2200 2100 1000 1850 350 Table 5 Distance between customer markets and delivery Hubs(km). DELIVERY HUBS CUSTOMER MARKETS W X Y Z W X Y Z 1 1400 1450 2200 1400 1 2000 1300 1450 2000 1 1650 2200 1300 1650 1 Table 6 Distance between delivery hubs and Landfill(km). The social objective functions need only two data sets. The number of labourers employed at each of the possible warehouses and the number of harms at work that would occur due to the different technologies. The latter, again simplified by taking as 10% for non-automated warehouses and 2% for automated warehouses. Both units are measured in work days that are utilised or lost. So, the no. of jobs must be multiplied by 30, considering our period is one month, and the harms would set each worker on an average by 2 work days, so they will be multiplied by 2. The data about jobs created and harms at work is given in Table 9. The environmental impact for processing at the warehouses is directly proportional to the electricity consumer per unit processed. The same is mentioned in Table 10. 6. Experimental results Microsoft Excel’s Solver Tool was used to solve the above case. By using this it was clearly identified which delivery centres, mother warehouses, landfills and recycling centres are selected to reduce all the three costs i.e. economic, environmental and social. The results obtained are plotted in the form of graphs. Graphs are plotted while varying one objective goal value and keeping the other two as constants. Following are the results obtained. LANDFILL DELIVERY HUBS W X Y Z W X Y Z 10 1400 1450 2200 1400 10 2000 1300 1450 2000 10 1650 2200 1300 1650 10 Table 7 Distance between delivery hubs and recycling Centres(km). RECYCLING CENTRE DELIVERY HUBS W X Y Z W X Y Z 50 1400 1450 2200 1400 50 2000 1300 1450 2000 50 1650 2200 1300 1650 50 6.1. Varying Economic Objective Goal On keeping the environmental and social objective goals to be constant and varying the Economic objective goal, below results were observed. The values of environmental and social goal were kept constant were taken from the iteration 1 of Table 11. The economical goal was changed regularly to see what changes it will create in opening of facilities. Fig. 4 shows the results that were simulated for delivery hubs, mother warehouses, landfills and recycling centres. For e.g. for economic goal of 67,00,00,000 it can be seen that for delivery hub H1T1, H2T1, H4T2 are too be opened as delivery hub with technology, W1T2 and W2T1 are opened as mother warehouse with technology. No landfills destination that are too be opened and N2, N3 are the recycling center that too be opened. It can be see observed in case of delivery hubs, delivery hub 2 with the second technology i.e. H2T2 and delivery hub 3 with the Table 8 Processing Cost per Unit for Delivery Hubs with both Technologies. Delivery Hub options W X Y Z Tech 1 Tech 1 Wages(Rs) Electricity Consumed(Kwh) Processing Cost per Unit(Rs) Wages(Rs) Electricity Consumed(Kwh) Processing Cost per Unit(Rs) 1800000 1800000 1250000 1250000 150000 150000 125000 125000 13 13 11 11 1125000 1125000 781250 781250 225000 225000 187500 187500 9 9 7.75 7.75 P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348 9 Table 9 Jobs created, and harm accrued in work days. Delivery Hub options Jobs (Work Days) T1 Jobs (Work Days) T2 Harms (Work Days) T1 Harms (Work Days) T2 W X Y Z 1800 1800 1500 1500 1125 1125 937.5 937.5 12 12 10 10 2.4 2.4 2 2 Table 10 Environmental Impact of processes and transportation. Clothes considered in 1 kg 2 Environmental Environmental Environmental Environmental Environmental Environmental Environmental Environmental Environmental Environmental Environmental 140 0.07 34 0.017 4.2 2.1 8.2 4.1 100 60 .6 impact impact impact impact impact impact impact impact impact Impact Impact in transportation to DH, LF and RC per ton in transportation to DH, LF and RC per item in transportation to FC per km in transportation to FC per item in Processing at Landfill per kg in Processing at Landfill per item in Processing at Recycling Centre per kg in Processing at Recycling Centre per item in Clothes processed per Kwh electricity consumed due to electricity per Kwh per cloth processes Table 11 Total costs and selection of hubs, warehouses, landfill and recycling centres. - Iteration 1 Iteration 2 Iteration 3 Iteration 4 Iteration 5 Iteration 6 Iteration 7 Iteration 8 Economic Goal Environmental Goal Social Goal Economic Cost Environmental impact Social Cost Delivery Hubs Opened 670000000 27000000 10000 670000000 23173066 10223 H1T1, H2T1,H3T2, H4T2 W1T2, W2T1 660000000 26000000 9500 659999852 19367352 9557.6 H1T1, H2T2,H3T2, H4T2 W1T2, W2T1 650000000 25000000 9000 629305242 25000020.2 9140.7 H1T2, H2T2, H4T2 W1T1, W2T1 640000000 24000000 8500 638942809 23999998.4 8953.6 H1T2, H3T2, H4T2 W1T1, W2T1 630000000 23000000 7500 598768560 22999998.2 7956.7 H1T2, H2T2, H4T2 W1T1, W2T2 620000000 22000000 7000 606989662 22000000.7 7769.6 H1T2, H3T2, H4T2 W1T2, W2T1 610000000 21000000 6500 609995405 20999950 6585.6 H1T2, H3T2, H4T2 W1T2, W2T2 600000000 20000000 6000 533630033 19999997.4 8103.5 H1T1, H2T1, H4T2 W1T2, W2T2 e N3,N4 e N3,N4 e N1,N4 L1,L3,L4 e L3 N1,N2 L3,L4 N1 L1 N3,N4 L1,L2,L4 e Mother Warehouse Opened Landfills Recycling Center first technology i.e. H3T1 were opened in most number of the cases. In case of the mother warehouses it could be stated that mother warehouse with first technology was opened in all cases except one. With changing economic goals, the landfills were less preferred compared to the recycling centres. In case for the recycling centres N3, N4 are opened in most cases for most of the economic goals. 6.2. Varying Environmental Objective Goal Fig. 4. Varying economic objective goal. In this example by keeping the economic and social objective goals to be constant and varying the environmental objective goal, following results were found. Fig. 5 shows the results that were simulated for delivery hubs, mother warehouses, landfills and recycling centres. The values of economic and social goal were kept constant were taken from the iteration 2 of Table 11. The environmental goal was changed regularly to see what changes it will create in opening of facilities. In Fig. 5 for example for environmental goal of 27,00,00,000 it can be seen that for delivery hub H1T1, H2T2, H3T1 are opened as delivery hub with technology, W1T1 and W2T1 are opened as mother warehouse with technology. L1 as the sole landfill destination that too be opened, with N2, N3 are the recycling center that too be opened. It can clearly be seen that in case of delivery hubs, delivery hub 1 with the first technology i.e. H1T1 and 10 P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348 Fig. 5. Varying environmental objective goal. delivery hub 2 with the first technology i.e. H2T1 were preferable in most of the environmental goal varying. In case of the mother warehouses it is seen that mother warehouse two with technology i.e. W2T1 was opened in all cases irrespective of the environmental goal. Landfill L1, L4 was preferred landfills that were opened. Recycling centres N3 and N4 are selected more as compared to the other two centres. 6.3. Varying Social Objective Goal As shown in Fig. 6, - the economic and environmental objective goals were kept constant and the social objective goal was varied. The social goal was changed regularly to see what changes it will create in opening of facilities. In Fig. 6 for example for social goal of 10,000 with the rest values of economic and environmental goal is taken from iteration 10 of Table 11. It can be seen that for delivery hub H1T1, H2T2, H3T1 and H4T2 are opened as delivery hub with technology, W1T2 and W2T2 are opened as mother warehouse with technology. No landfills are too being opened and N2, N3, N4 Fig. 6. Varying social objective goal. are the recycling center that was too be opened. It is found that delivery hub 4 with technology 2 in all cases irrespective of social goal. Among the mother warehouses W2T21 was selected in most of the cases. Landfill L3 was the only landfill that too be opened with changing social goals, while N4 was the recycling centre opened in all cases. Weights were also assigned to all the three objective goals to check whether it would influence selecting the hubs, warehouses, landfills and recycling centres. Where W1 is the weight assigned to the Economic goal, W2 is the weight assigned to the environmental objective goal and W3 is the weight assigned to the Social goal. The values were taken from the iteration 1 of Table 11. Fig. 7 shows the results that were obtained on assigning different weights to the objective functions, the different weights were run on the goal values of 1st iteration from Table 11. The figure depicts how varying the weights could impact the decision of opening different facilities. Fig. 7. Varying weights for objective functions. 680000000 12000 660000000 10000 640000000 8000 620000000 6000 11 Social Goal Economic Goal P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348 600000000 4000 580000000 400000000 2000 600000000 0 6000 Econmic Cost 7000 Social Cost 8000 9000 10000 11000 Fig. 8. The effect of (a) Economic goal Vs Economic Cost (b) Social Goal Vs Social Cost. The Table 11 indicates how varying the costs and goals is influencing the closing and opening of the delivery hubs, warehouse, and recycling centres. While in all iterations the no of delivery hubs remained either two or three, the no of mother warehouse to be opened remained constant with two warehouses in each iteration. The only changes apart from the total cost incurred was the varying the no of landfilling centre and the recycling centres to be used. In iteration 5 there is a one landfill center and two recycling centres while in iteration 8 there are only landfill and in iteration1 there are only recycling centres which has led to the steep rise in the overall cost. There are trade-offs in each iteration among the three costs and a perfect balance is to be decided by the manager by taking the future environmental impacts and social responsibility into account. The Fig. 8(a) shows Economic goal vs economic cost and with increasing goal the economic cost also increases steadily then after a point the rise in economic cost slows down with increase in economic goal. Fig. 8(b) shows Social goal versus social cost and both the goal and cost are increasing together, henceforth then economic goal decreases with the rising social goal to accommodate the target social target and to accommodate a small increase in the social goal requires a high increase in the cost once the social target is reached. minimum probability of ai and the constrained (9) can be defined as follows. 6.4. Uncertain Return Market This study has significant prospects and implications for SC Managers and even the leadership of online retailing companies. Online retailing is still a relatively new industry in India and managers are yet to explore the many distinct opportunities that are present to optimize costs and other factors. This study is first of its kind where the network designed in the context of e-commerce and numerical example which is based on online retailer for cotton based clothing is specific to the Indian context from where the managers can take a clue of implementing it to their firm by varying the goals as per their firm need and expanding the model as when required, by keeping the multiple objectives as per the model. The In real situation, the return product market may be uncertain and in such scenario, the exact amount of return products may not be known with certainty. In order to capture this uncertainty in the model, it was assumed that the return products capacity in the b Þ with specified “Return Market” follows normal distribution ( b m mean and standard deviation (10% of its mean). Thus the associated constrained of expected market return would become stochastic in nature. For this-chance-constrained programming (Charnes and Cooper, 1959) was used where the constraint i is realized with a P X bmh bb m am c m; h (21) h The following Table 12 provides the results of different values for economic cost, environmental cost and social cost for different values of goals with varying chances or probability of realization of the constraints. Then compared the uncertain case with certain market return and it was found that the percentage changes in social cost is quite high whereas the changes in environmental cost is insignificant. The Fig. 9 compares the cost with different probabilities for the chance constraints in certain vs uncertain market, viz.; (a) Economic costs in uncertain case was higher in most of the cases, (b) Environmental cost showed a varying trend while with the first set of goal the environmental cost in uncertain market was higher or equal to the certain market while with the second set of goals certain market cost was higher and (c) Social cost in uncertain market was always higher than the certain market in all the scenarios. 7. Managerial implications and conclusions Table 12 Comparing costs in Uncertain Vs Certain Market. Uncertain Market return Costs EC EVC Certain Market return SC % change in costs due to uncertainty EC EVC SC % Eco. Change %Env. Change %S ocial change 659999852 659999852 659999852 659999852 19367352 19367352 19367352 19367352 9557.6 9557.6 9557.6 9557.6 0.8766419 0.293556 0.0011398 0.000268 1.0049469 0.0625489 0.0533603 0.074881 30.95337742 30.95337742 30.95337742 30.95337742 629305242 629305242 629305242 629305242 25000020 25000020 25000020 25000020 9140.7 9140.7 9140.7 9140.7 3.2884373 4.5696649 3.2885755 4.8772578 0.40922 0.829462 0.871984 0.973594 18.63424027 18.63424027 18.63424027 18.63424027 1-a Goals: Eco-660000000,Env.-26000000, Social:-9500 0.8 665785687 26000000 12516 0.9 658062383 19780174 12516 0.95 660007375 19719530 12516 0.99 659998081 18873139 12516 Goals: Eco-650000000,Env.-25000000, Social:-9000 0.8 649999550 22424775 10844 0.9 658062383 19780174 10844 0.95 650000420 19512579 10844 0.99 659998081 18873139 10844 12 P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348 A model to set up reverse logistics supply chain is formulated in this paper. This paper specifically caters to the existing and upcoming e-commerce firms aid in decision making towards setting up of their RL. The novelty of this paper lies in that it combines the concepts and ideas of Sustainability, e-commerce in the Indian context, and RL, with a focus on returns management. The SC structure used here is a compact and combined version of the one being used by existing Indian e-commerce firms and sections derived from literature covering concepts relating to the triple bottom line approach. GP was used to solve this model. A numerical example was formulated, the data for which was determined using inputs from experts from the online retailing industry and a specific firm in Mumbai, dealing in clothing accessories like t-shirts. The solution was obtained using Microsoft Excel. The solution was tested by varying the goals for each objective vehemently and changing weights for different goals to get the full extent of the flexibility of the model. implementation of this will help managers optimize their supply chain to be sustainable as firms are coming under pressure to reduce their carbon footprint, increase their social deliverance to the society and hence by adopting the important triple bottom line approach, striking the right chord by balancing the economic, environmental and social costs as while optimizing the total cost he has to keep in mind the environmental impacts and social responsibility into account. This study will help the managers in deciding the number of facility stores and warehouses needed to open and operate, technology to be adopted for a more efficient way of transferring and processing products. The strategic long term decisions could then be taken relating to the opening of new warehouses and which technology to be adopted for the respective delivery hub and mother warehouse. The numerical example will help to identify the different weights to give to different objectives while minimizing the costs, maximizing the social responsibility and achieving the set goals. Uncertain Vs Certain Market 670000000 660000000 650000000 640000000 630000000 620000000 610000000 0.8 0.9 0.95 0.99 0.8 Uncertain EC 0.9 0.95 0.99 Certain EC (a) Uncertain Vs Certain Market 30000000 25000000 20000000 15000000 10000000 5000000 0 0.8 0.9 0.95 0.99 Uncertain EnvC 0.8 0.9 0.95 0.99 Certain EnvC (b) Uncertain Vs Certain Market 15000 10000 5000 0 0.8 0.9 0.95 0.99 0.8 Uncertain SC 0.9 0.95 0.99 Certain SC (c) Fig. 9. Cost comparison with different probabilities for the chance constraints in certain vs uncertain market: a) Economic costs b) Environmental costs c) Social costs. P. Dutta et al. / Journal of Cleaner Production 249 (2020) 119348 There exists plenty of scope for further research on this topic. Studies could be done firstly, combining forward and reverse logistics to obtain a complete and definitive model. Next, a more varied range of products could be used to test out the model. Additionally, this could be made a multi-period model and to duplicate life-like scenarios, certain factors like total and individual demands of the customer market could be made uncertain in nature. Author contribution Pankaj Dutta: Conceptualization, Writing- Original draft preparation and Supervision. Anurag Mishra. Methodology, Formal analysis and WritingReviewing. Sachin Khandelwal: Software, Validation, Data curation. Ibrahim Katthawala: Editing, Visualization. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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