Fuzzy Optimization with Resilience Metrics for Sustainable Supply Chain Planning under Uncertain and Disruption Environments

In the evolving landscape of modern supply chains, achieving sustainability while ensuring resilience presents a significant challenge. This study introduces a novel fuzzy optimization technique designed to develop sustainable supply chain plans under uncertain environments. The proposed framework integrates the principles of Chance-Constrained Programming (CCP) with Intuitionistic Fuzzy Linear Programming (IFLP), enabling decision-makers to manage the risk of constraint violations while simultaneously addressing levels of satisfaction and non-satisfaction. By incorporating resilience metrics, the model evaluates its capacity to respond effectively to disruptions and uncertainties, ensuring robust operational performance characterized by flexibility, redundancy, and recovery capabilities. The methodology aims to optimize sustainable supply chain planning by minimizing total costs and maximizing social and environmental performance scores, while accounting for imprecise costs and customer demands. A case study demonstrates the practical application of the model, emphasizing its efficacy in addressing sustainability and resilience challenges in real-world scenarios. The findings highlight the potential of fuzzy optimization to enhance decision-making processes in sustainable supply chain management.