Designing sustainable closed-loop supply chain network with considering spot-to-point inflation and carbon emission policies: A case study

Abstract

This paper uses a fuzzy robust stochastic optimization (FRSO) approach and a new hybrid algorithm of genetic algorithm (GA) and multi-choice goal programming with Utility Function (MCGP-UF) to minimize environmental impacts (EI) while maximizing net present value (NPV) and social impacts (SI) in a sustainable closed-loop supply chain (SCLSC) in a case study has been proposed under hybrid uncertainty with considering spot-to-point inflation (STPI). The resulting mixed-integer linear programming (MILP) model has been applied to the electronic industry. The proposed model is evaluated in several experiments and discussed in different scenarios to prove the efficiency and performance comparison and validation of the proposed method. The results of this study double the share of the article (i) Effective improvement of the obtained solutions by reducing the solution time by 25% showed that the proposed method can be responsible for large-scale problems. (ii) Sensitivity analysis on parameters such as demand, inflation, costs, and capacity showed which scenario has the most impact on the strategic and tactical decisions of companies.