A Multi-Objective Optimization Model for Sustainable Supply Chain Network Design under Uncertainty

Abstract

Under the global carbon neutrality agenda and increasing supply chain uncertainties, designing supply chain networks that balance economic, environmental, and social sustainability has become a strategic priority for enterprises. This paper proposes a Robust Optimization-based Multi-Objective Mixed Integer Programming model (ROMO-MIP) to address sustainable supply chain network design under uncertainties such as demand fluctuations, carbon price volatility, and supply disruptions. The model simultaneously optimizes three objectives: minimizing total cost, minimizing carbon emissions, and maximizing job creation and regional equity. The -constraint method and the NSGA-III algorithm are employed to generate the Pareto front, and an empirical case study is conducted on the lithium-ion battery supply chain for new energy vehicles in China. Results show that incorporating robustness parameters significantly enhances network resilience under uncertainty, while multi-objective trade-off analysis enables decision-makers to select optimal configurations aligned with policy priorities. This study provides a practical decision-support tool for firms seeking green transformation and resilient operations.