A Performance-Oriented Optimization Framework Combining Meta-Heuristics and Entropy-Weighted TOPSIS for Multi-Objective Sustainable Supply Chain Network Design

The decision-making of sustainable supply chain network (SSCN) design is a strategy capacity for configuring facility and product flow. When optimizing conflicting economic, environmental, social performance objectives, it difficult to select the optimal scheme from obtained feasible decision schemes. In this article, according triple bottom line sustainability, multi-objective optimization model developed, novel performance-oriented framework proposed. This framework, referred as integrates meta-heuristic algorithms entropy-weighted technique order preference by similarity an ideal solution (EW-TOPSIS). can comprehensively evaluate overall SSCN EW-TOPSIS guide evolution process algorithms. decision-makers obtain schemes calculated meta-heuristics determine one value evaluated EW-TOPSIS. article combines three evaluation strategies with four algorithms, namely, non-dominated Sorting Genetic Algorithm-II (NSGA-2), differential evolutionary (MODE), particle swarm (MOPSO), gray wolr (MOGWO), verifying effectiveness framework. results validate that proposed has much better sustainability than traditional methods. Furthermore, provide managers special best performance. Finally, some research prospects are presented such more multi-criteria making