Supply chain network design with flexibility, resiliency, and sustainability

Supply chain network designs (SCNDs) have gained significant popularity in recent years as a means to reduce overall supply chain (SC) costs and establish a competitive edge. A flexible supply chain network (FSCN) holds promise for effectively managing SC complexity and optimizing total costs by eliminating unnecessary nodes and central hubs. This study develops a multi-objective mathematical model that integrates flexibility, resiliency, and sustainability dimensions within supply chain network design (SCND). The proposed model simultaneously optimizes three conflicting objectives, i.e. total cost, supply chain resilience, and environmental emissions, while addressing demand uncertainty through a scenario-based approach. To generate high-quality Pareto solutions, two multi-objective meta-heuristic algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Simulated Annealing (SA), are employed. The Taguchi analysis is subsequently employed to fine-tune the meta-heuristic parameters. Numerical experiments demonstrate that the solutions generated by MOPSO outperform SA, yielding a remarkable 46% increase in total cost benefits. Sensitivity analysis reveals that the most critical parameters are the number of days in inventory and production cost. The findings underscore the scientific contribution of this study by providing a comprehensive and adaptive framework for designing flexible and resilient SCs.