A Stochastic Multi-Criteria Decision-Making Framework for Optimizing Supply Chain Resilience
Keywords:
Supply Chain Resilience, Stochastic Multi-Criteria Decision-Making, Probabilistic Modeling, Real-time Data Analytics, Operational Continuity, Risk Mitigation, Global Market DynamicsAbstract
In the rapidly evolving landscape of global supply chain management, decision-makers face unprecedented challenges in optimizing resilience. This study introduces a stochastic multi-criteria decision-making (MCDM) framework that integrates probabilistic modeling with advanced linear programming to enhance decision efficacy. Utilizing real-time data analytics, the framework facilitates the anticipation of potential disruptions, offering strategic pathways for resilience enhancement. Our results demonstrate significant improvements in supply chain robustness, underscoring the efficiency of the proposed model in real-world applications. The insights derived from this work offer a substantial contribution to the field of management science, providing a robust tool for practitioners aiming to fortify their supply chain resilience strategies.
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