A Fuzzy Robust Programming Model for Sustainable Closed-Loop Supply Chain Network Design with Efficiency-Oriented Multi-Objective Optimization

Abstract

Sustainable closed-loop supply chain (SCLSC) network design and decision-making is a critical problem for enterprises and organizations’ operations because of its excellent economic, environmental, and social performance. This article proposes a multi-objective mixed-integer programming model with targets for minimum total cost, reduction in environmental damage, and maximum social responsibility. In order to deal with the uncertainty caused by the dynamic business environment, a fuzzy robust programming (FRP) approach is applied. Furthermore, an efficiency-oriented optimization methodology, hybridizing meta-heuristics and efficiency evaluation, is proposed to solve the developed multi-objective model and functions as auxiliary decision-making. Data envelopment analysis is applied to evaluate the sustainability performance of feasible solutions and calculate their efficiency. The efficiency can comprehensively reflect the sustainability performance and guide the evolution process of meta-heuristic algorithms. A numerical case validates the proposed FRP model and efficiency-oriented optimization methodology. The results demonstrate that with the proposed methodology, decision-makers not only can obtain a set of efficient schemes but also can determine the optimal scheme with the best sustainability performance.

Flexible fuzzy-robust optimization method in closed-loop supply chain network problem modeling for the engine oil industry

“…Fuzzy programming has been used to control uncertainty parameters through fuzzy triangular numbers. Guo et al (2022) proposed a multi-objective mixed integer programming model with the objectives of minimum total cost, minimization of environmental damage, and maximum social responsibility. They used a robust fuzzy programming approach to deal with the uncertainty caused by the dynamic business environment.…”