Resource depletion and e-waste generation through electronics use have reached an alarming stage in developing countries, posing a serious threat to the environment and human health (Wath, Dutt & Chakrabarti 2011:260). During the last decade, there has been an exponential increase in the use of electronics in India, and most of these are discarded irresponsibly by consumers (Wath et al. 2011:252). In the framework of sustainable development, discarding the electronics products that are no longer in use by their original users is not a viable option (Achillas et al. 2010:870). Reverse logistics (RL) has gained immense relevance in this regard, as it plays a crucial role in utilising the residual value still existing in the discarded electronics. Disposing off the unwanted parts and materials responsibly is having a positive impact on the environment (Khor et al. 2016:97; Mutha & Pokharel 2009:334). Though government regulations and policies in India require Original Electronics Manufacturers (OEMs) to incorporate sustainable recovery strategies for extending the entire life cycle of their products, consumer returns are still not managed by manufacturers but mostly by an informal sector involving ‘kabadiwallahs’, recyclers and dismantlers (Dwivedy & Mittal 2012:230). Lack of government support and awareness among manufacturers, as well as consumers, are some of the major reasons for the disinterest of OEMs in India towards developing an efficient recovery system (Ravi & Shankar 2015:887). The implementation of RL can be a costly endeavour as it involves the processes of collection, refurbishing, disassembly and reprocessing. Therefore, OEMs prefer outsourcing all the RL operations to specialised third-party reverse logistics providers (3PRLPs) (Subramoniam, Huisingh & Chinnam 2010:1577). However, firms can seek to perform the RL operations independently or jointly with 3PRLs to gain maximum benefits from the returns (Agarwal et al. 2016:481).

Recent years have witnessed the growing interest of OEMs in redesigning their logistics network for managing the RL activities sustainably. Although deriving maximum economic benefits from the returns is the primary objective of RL, it inherently helps in creating a positive impact on the environment and society. Incorporating strategic recovery decisions within their logistics network can also help manufacturers enhance their bottom line. To begin with the process of implementation, it is fundamental for the OEM to focus on establishing new recovery facilities (RFs) and expanding existing facilities. The selection of location for setting up of facilities across the supply chain (SC) is an important decision, as it entails long-term cost obligations on the firms (Ertuğrul 2011:725). The right choice of location can assist the firm in gaining a competitive edge while simultaneously improving on the operational performance, not only in the short term but also in the long term. In this regard, the present article aims to contribute further to the area of RL network design problems, integrating optimal facility location decisions for creating a sustainable channel for consumer returns. Furthermore, in view of the growing concern for sustainable development, it has become increasingly important for the firms to incorporate all the sustainability factors in facility location decision making (Chen, Olhager & Tang 2014:155). In this context, the main focus of the present work is to develop a mathematical model for designing a sustainable RL network from the OEM’s perspective wherein the collection, inspection and repairing of returns is done by the OEM independently, and the other RL operations including disassembly, recycling and disposal are outsourced to 3PRLPs. For the purpose of establishing new centres for repair and refurbishing activities, few collection centres (CCs) are chosen for accommodating these activities. It would require appropriate expansion of the CCs depending on space availability, cost of expansion, available budget and the number of returns. The notable feature of the model is the evaluation of CCs under financial, environmental and social considerations and integration of the facility selection decisions within the network design. The CCs are evaluated taking into account qualitative and quantitative criteria based on the three dimensions of sustainability, using integrated multi-criteria decision making (MCDM), which combines the efficiencies of the Analytical Hierarchical Process (AHP) and the Complex Proportional Assessment (COPRAS) method. A fuzzy mixed integer linear programming model is proposed for determining the optimum number, locations and capacities of the RFs, penalty cost for under-utilisation of the capacities and number of returns to be repaired at the selected RFs. Fuzzy programming approach is used for effectively obtaining a properly efficient solution, which satisfies the decision maker’s (DM’s) desired aspiration levels for the conflicting objectives of minimising the cost and maximising the sustainable performance of selected RFs. As per our knowledge, an optimisation model for an integrated RL network design focussing on sustainable evaluation and selection of RFs, capacity expansion as well as penalty for under-utilisation of capacities of selected RFs, along with flow allocation decisions has not been considered in the previous studies. Another notable contribution of the present work is that the model is illustrated using a case study of an electronic manufacturing firm in India. The fuzzy decision-making model can be effectively used by SC managers of the firm for configuring a sustainable RL network with conflicting goals.

The rest of the article includes the relevant literature review, the problem definition, and the proposed RL network followed by the proposed methodology. A brief description of the fuzzy programming approach is presented, which is applied to validate the proposed RL model using the data set of a real case study. Furthermore, the results are discussed, and finally, the concluding remarks are provided in the end.

In the last few years, the growing ‘take-back’ laws are challenging the manufacturing firms to redesign their logistics network to incorporate RL into their network. Although there is a plethora of literature on RL network designing (Darbari et al. 2015:2; Dehghanian & Mansour 2009:560; Ilgin & Gupta 2010:567; Kannan et al. 2012:76), most of the studies in the literature have focussed on establishing RL network designs only from a 3PRLP’s perspective (Mahmoudzadeh, Mansour & Karimi 2011a:338; Mahmoudzadeh et al. 2011b:30; Min & Ko 2008:176), while the OEM’s perspective has been not been considered. From the OEM’s perspective, there are various strategic, tactical and operational decisions that the OEM must ponder upon, for creating an effective RL network (Aras et al. 2015:325). The strategic choices have long-term significance on the recovery network. These include network design models comprising the location of the CCs (Aras, Aksen & Tanuğur 2008:1223), inspection centres (Alumur et al. 2012:71), recycling centres (Aras et al. 2015:324), remanufacturing facilities (Diabat, Abdallah & Henschel 2015:245), et cetera. The second class of decisions are tactical decisions that have a medium-term impact on RL. These decisions include policies for inventory management of returned products (Zhang 2013:598), transportation decisions (Shaik & Abdul-Kadir 2013:495), et cetera. Finally, there are operational decisions that impact the RL on a short-term basis. These include decisions pertaining to the flow of products, components and material across various facilities of the network (Darbari, Agarwal & Jha 2016:791).

Deciding on the locations of warehouses, CCs and repair facilities are key aspects of the strategic plan of reverse SC configuration. However, the decisions regarding the facility locations must not be made in isolation but must be integrated within the RL network design to achieve an efficient recovery system. Many researchers have focussed on developing mathematical models for configuring logistics network design integrating facility location decisions and RL functions (Achillas et al. 2010a:2594; Alumur et al. 2012:67, 2015:419; Assavapokee & Wongthatsanekorn 2012:129; Gomes, Barbosa-Povoa & Novais 2011:1645; Xianfeng, Jianwei & Meilian 2010:403; Zhang & Lee 2013:1348). Most previous studies have focussed only on economic considerations and there is a lack of research addressing social and environmental issues in facility location problems within the context of RL network design (Abdessalem, Hadj-Alouane & Riopel 2012:139; Kim & Lee 2013:1132; Queiruga et al. 2008:182; Tari & Alumur 2014:157). Very few studies have considered factors of evaluation, which address all three dimensions of sustainability (Achillas et al. 2010b:870; Temur, Kaya & Kahraman 2014:591; Zhou & Zhou 2015:61). Although a lot of research has been done on the aspects of facility location selection within the domain of RL network designing, selection of CCs as RFs based on all three indicators of sustainability, particularly in the context of the Indian electronics sector as done in the present article, has been missing in the literature. In addition, the novelty of the present research work also lies in developing an integrated RL optimisation model, which can capture a trade-off between the conflicting objectives of minimising cost and maximising the sustainable performance of the RFs, while making crucial facility location and flow allocation decisions.

The present RL network of the company consists of retail zones each having a designated CC, which is the collection point for the entire zone. All the returns consolidated at the CCs are collected by a 3PRLP, which pays a fixed price for all the returns irrespective of the quality. The 3PRLP then takes responsibility of the returns and the company’s legal duty of handling its returns is fulfilled. Because of customer pressure, market competence and environmental consciousness, the company aims to act more responsibly and design its RL network so as to take control of the processes independently or jointly with service providers. To realise the potential of huge economic benefits from the sale of secondary products, the company plans to perform the processes of collection, inspection, repairing and redistribution to secondary markets (SM) on its own. Strategic decisions are to be made regarding how many new RFs are required and where they should be located. Official dismantlers and recyclers must manage the process of dismantling, recycling and proper disposal as per the laws mandated by the government (Borthakur & Singh 2012:360). Therefore, these activities are outsourced to 3PRLPs. The company in association with non-governmental organisations (NGOs) seeks to play an influential role in providing opportunities to the lesser privileged section of the society. As a consequence, it plans to donate some of its returns that are in reasonable good working condition to those in need through the NGO. This initiative is to be carried out at the RFs. In view of this, a fuzzy decision-making model is proposed for developing an economically, environmentally and socially sound RL network, which addresses the following concerns:

Quick collection and redistribution to ensure a steady flow of returns across every facility.

The proposed RL network designed to address the above concerns consists of CCs, an integrated dismantling centre (DMC), a SM, a scrap yard and an NGO as illustrated in Figure 1. Because all the recovery options were previously managed by 3PRLP, the company was losing out on the revenue generated from selling the repaired products at the SM, which is a substantial amount. In this regard, the company plans to take charge of the repairing of products. Repairing of electronic returns requires replacement and refurbishment of parts to renovate the product so that it can be easily reused. It is a manual process that can be handled by the trained staff of the company. Opening new RFs for repairing and refurbishing is not required as the process can be easily managed manually by a team of skilled technicians and can be carried at CCs with proper expansion of space and infrastructure. However, because the number of returns to be repaired and refurbished is not significantly large, not all CCs are to function as RFs. The selection of a CC as RF is to be made based on a number of sustainable factors and requires a careful analysis of the existing scenario and future plans.

The flow of returns of the above network begins with the collection and inspection process at CCs. The initial level of inspection determines the buyback value based on the working state, type and age of the model. The returns are then segregated for repair, donation to an NGO, dismantling or sent to the scrap yard. The returns not fit to be reused or donated are to be disassembled for recovery of parts and materials and sent to the DMC for part recovery or to the scrap yard for material recovery or safe disposal. The DMC is third-party owned, and the returns to be dismantled are collected by the 3PRLP from the CCs. Legal recyclers who take care of the discarded returns and parts for valuable material recovery and safe disposal manage the scrap yard.

The methodology adopted for configuring the proposed network can be outlined as follows:

For selection of CCs as RFs, the CCs are evaluated under a comprehensive list of sustainable criteria using the AHP-COPRAS method. The output of the AHP-COPRAS method is the utility of each CC that represents the relative performance of one CC over the other.

The case study considered in here is of an electronics manufacturing company located in Delhi National Capital Region, India. The company has 8 CCs located in the region that carry out the initial collection as well as inspection of the returned products. The DMC is situated at Mayapuri, and the CCs are located at East of Kailash (A1), Vaishalli (A2), Noida (A3), Dwarka (A4), Vasant Kunj (A5), Sushant Lok (A6), Karol Bagh (A7) and Model Town (A8) as shown in Figure 2. A team of company stakeholders are assigned the task of evaluation of the expansion of the CCs to incorporate the repairing, refurbishing and repackaging processes.

For the purpose of evaluation, the relative importance of the criteria is derived using the AHP as shown in Table 1. The components of the eigenvector represent the weights of the criteria. Non-beneficial criteria are marked as (−) and the beneficial criteria are marked as (+).

These weights are utilised in the initial decision matrix of the COPRAS method as shown by Table 2. The columns of the matrix correspond to the performance of the alternatives (CCs) for each criterion. The column for criteria C1 indicates the distance calculated as the sum of the distance of CC from the DMC and its distance from the centre of gravity of all CCs (Values from Table 3 are used for the calculation). Column C2 is the running cost, which is the monthly cost for carrying the operations at CC. C3 is the per square feet rental cost of CC. C4-customer service level and C7-technological capability are measured using a scale of 1–10 where higher values signify higher performance. In the absence of quantitative data for criteria C5, C6 and C8, the AHP is used for comparing the alternatives for each criterion using the derived priority vectors.

Following steps 1–7 of the AHP-COPRAS method, the weighted normalised matrix is derived and the priority value Qi and utility Ui are computed. Table 4 represents the final matrix of calculation.

The inference drawn from Table 4 is that A4 is the best choice among the alternatives. A7 is the second best and third and fourth choices are A8 and A2. The last column represents the utility of each CC, which reflects the relative sustainable performance of each CC. This column vector is used as an input parameter for solving the proposed mathematical model.

The data set provided by the company is as follows: The fixed cost of expansion of each CC is Rs. 150 000, Rs. 100 000, Rs. 120 000, Rs. 100 000, Rs. 110 000, Rs. 130 000, Rs. 130 000 and Rs. 110 000, respectively, while the per square foot cost of expansion is Rs. 110, Rs. 60, Rs. 60, Rs. 35, Rs. 100, Rs. 65, Rs. 70 and Rs. 60, respectively, for each CC. The repair cost per unit product at each CC is Rs. 130. The per kilometre transportation cost is Rs. 10, the penalty cost per unit is Rs. 20 and the total budget for expansion is Rs. 450 000. The lower limits for the capacity of the CCs are 180, 250, 150, 250, 180, 170, 185 and 240, respectively, with the upper limits set as 350, 200, 400, 250, 250, 300 and 260, respectively. The distance of each CC from the dismantling unit at Mayapuri (in km) is 9.4, 13.5, 12.4, 4, 5, 10.5, 4 and 6.4, respectively.

Government regulations, customer pressure and market competence are binding factors for electronics manufacturers to design a sustainable recovery model for consumer returns. The manufacturers have also started to realise the potential of RL and consequently are inclined to handle the returns themselves more effectively and responsibly. In this regard, a bi-objective fuzzy decision-making model is proposed in the study, which can serve as a decision tool for the Indian manufacturers in designing a sustainable RL network for managing end-of-life and end-of-use returns. It is a two-phase model where the first phase involves using a combined AHP-COPRAS methodology for evaluation of the CCs for carrying the repair and refurbishment process. The final selection is done in the second phase with the aid of a mixed integer fuzzy linear programming formulation, which effectively determines the number, location and capacity of RFs, the penalty cost and the number of returns to be repaired at each selected facility. Fuzzy programming approach is used effectively in obtaining a properly efficient solution that satisfies the DM’s desired aspiration level for the goals of minimising cost and maximising the sustainable performance of the RFs. The notable feature of the model is the evaluation of CCs under financial, environmental and social considerations and integration of the facility selection decisions with the network designing. This study indicates through the results discussed how an electronic firm in India can earn a sustainable system for their returns in the most cost-efficient way. The study can further elaborate on the nature of collaboration with third-party providers and association with NGOs to develop a RL network that clearly defines and determines the role of each party in the functioning of the RL network.