Designing a resilient supply chain network under ambiguous information and disruption risk

In case of supply chain disruption following severe disasters, many supply chains tend to collapse and take a long time to recover. Resilient supply chain network design (RSCND) is an important research problem in supply chain management, which means that the supply chain can maintain continuous supply and quickly restore the supply capability in part destruction. Based on the limited distribution information of uncertain demand, a two-stage distributionally robust optimization (DRO) model with ambiguous chance constraint (ACC) is proposed to solve the RSCND problem under demand uncertainty and disruption scenario to provide decision support for planning the supply chain network. Finally, to verify the effectiveness and practicability of the proposed DRO model, we apply the method to a real case study in Wuhan, China, about designing a resilient RSC network to withstand disruption. By comparison and sensitivity analysis in numerical experiments, some management insights of industry decision-makers are obtained.

Automated summary

This study focuses on the design of resilient supply chain networks in the event of supply chain disruption. A two-stage distributionally robust optimization model with ambiguous chance constraint is proposed to address the problem under demand uncertainty and disruption scenario. The model is applied to a real case study in Wuhan, China, and provides decision support for planning a resilient supply chain network. The findings from numerical experiments and sensitivity analysis offer valuable insights for industry decision-makers.