Hybrid Knowledge-Guided Graph Learning and Distributionally Robust Decision Intelligence for Real-Time Supply-Network Reconfiguration under Cascading Disruptions and Demand Shocks

Supply-network disruptions can propagate rapidly across interconnected suppliers, plants, ports, carriers, products, and customers, making timely and reliable recovery decisions difficult. Existing data-driven approaches often focus on either prediction or optimization and may therefore overlook the combined effects of network structure, intervention uncertainty, and operational feasibility. This study proposes an integrated decision-support framework for supply-network reconfiguration under cascading disruptions and demand shocks. The framework combines network-aware learning, counterfactual response evaluation, uncertainty assessment, and robust optimization to compare alternative recovery strategies and identify feasible actions under changing disruption conditions. Experiments involving 9,215 logistics orders and 100,000 disruption events demonstrate a normalized weighted score of 0.948, supplier-change F1 of 0.947, policy value of 0.867, and resilience of 0.762. Compared with the historical policy, the proposed framework reduces expected cost by 33.73%, worst-case cost by 36.08%, revenue loss by 37.28%, and full recovery time by 29.52%, while maintaining 100% feasibility. These findings indicate that integrating network-aware prediction, uncertainty-conscious response evaluation, and robust decision optimization can improve the quality and practicality of supply-network recovery decisions under complex disruption conditions.