A Novel Fuzzy Goal Programming Approach with Symmetric Triangle Membership Functions for Closed-Loop Supply Chain Solutions

As the supply chains have become complex and more endurable, there is a need to optimize the CLSC to enhance the efficiency of a firm. Thus, this research proposes a method that uses Fuzzy Goal Programming (FGP) plus Symmetric Triangle Membership Functions (STMF) to capture the stochastic and the compromised nature of CLSC systems and to facilitate decision-making. The proposed approach applies FGP for dealing with one or more often conflicting objectives in CLSC, such as minimizing costs, maximizing customers’ satisfaction, or increasing environmental responsibility. These are used to work with the vague nature of the supply chain factors and goals to give a clearer picture of supply chain uncertainty than is possible with ordinary membership functions. This is illustrated through a case study of a multi-echelon CLSC network using a novel FGP model with STMF. The findings also show that the approach adopted here can address several objectives, namely, cost containment and environmental footprint, in the supply chain volatilities inherent in the business context. These properties enable the definition of a wide range of contingencies and decision-making improvements in response time whenever priorities or parameters are variable or uncertain. This concept brings a unique approach to closed-loop supply chain design and fulfills the needs of the decision-makers who have to make decisions based on both the tangible & intangible sides of the supply chain. Thus, it is revealed that by adopting the STMFs into the FGP model, it is possible to enhance CLSC systems' optimality and provide more workable solutions toward sustainability in the supply chain networks.