Resilient Network Design for Supply Chains with Emergency Events Based on Stochastic Programming

Supply chain resilience is crucial for supply chain network design. In the context of demand uncertainty, this study investigates the design of supply chain networks considering disruptions to primary suppliers and distribution centers caused by emergency events. With the objectives of maximizing resilience and minimizing total cost, the study explores optimal supply chain network designing with different combinations of four resilience strategies: multi-sourcing, emergency supply, direct delivery to stores, and retailer emergency inventory. The model is solved using two-stage stochastic programming and a Sample Average Approximation (SAA) method. Sensitivity analysis is conducted to evaluate the optimal results of resilient supply chain network design under different degrees of disruptions to primary suppliers and distribution centers, changes in market demand, and changes in maximum processing capacity of retailers. Finally, comparative analysis is conducted to determine the optimal strategy for resilient supply chain network design.