Allied closed-loop supply chain network optimization with interactive fuzzy programming approach

Abstract

The concept of closed-loop supply chain (CLSC) has started to attract growing attention due to the consumer pressures, environmental awareness, and legislations. Managers in many companies have realized that a well-designed supply chain (SC) can improve the companies’ performance in the market. Thus, a lot of companies start to focus on CLSC issues including remanufacturing, refurbishing, recycling, and disposal of end-of-life products. The body of literature on CLSC management has been overwhelmingly dominated by noncooperative studies. In order to fill up this gap in the literature, we deal with an allied SC network in cooperative environment. With the implementation of allied SCs, companies not only maximize their profit but also minimize their various costs and become more flexible and efficient in the market. Following this motivation, we develop a decentralized multilevel CLSC model for allied SCs. At the first decision level, the plants in allied SCs are considered as the upper-level DMs of the Stackelberg game. At the second level, raw material suppliers, common suppliers, assembly centers, and common collection centers are considered as the lower-level DMs of the Stackelberg game. In order to tackle each decision-maker (DM)’s unique objectives, we propose a new fuzzy analytic hierarchy process (AHP)-based interactive fuzzy programming (IFP) approach. In the IFP approach, upper-level DMs determine the minimum satisfactory level for their own objectives, and by using this value, the lower DMs evaluate their own satisfactory level. A compromise solution can be derived until termination conditions are satisfied. The primary aim of this study is to design a decentralized CLSC network in cooperative environment and to propose a novel IFP approach. Finally, a numerical example is implemented and analyzed in order to demonstrate the efficiency of the proposed approach. © 2017, Springer International Publishing AG.