A Distributionally Robust Fuzzy Optimization Approach for Resilient Manufacturing Supply Chain Network Design: An RCEP Perspective

Recent changes in trade barriers and increasing uncertainties in trade policies have forced companies to rethink their optimal supply chain settings. This article studies the impact of the regional comprehensive economic partnership (RCEP) agreement, particularly the cumulative rules of origin, on the resilient manufacturing supply chain network design problem with demand uncertainty. Three resilience strategies, namely, multiple sourcing, capacity redundancy, and technology innovation, are employed to improve supply chain resilience. Using type-2 fuzzy theory, we develop a distributionally robust fuzzy optimization (DRFO) model to address the proposed problem. In this model, the demand is represented as a parametric interval-valued fuzzy variable and its associated uncertainty distribution set. In terms of the model's tractability, we analyze the computational issues of the credibility constraint and reformulate the DRFO model into a computationally tractable mixed-integer linear program. Finally, we apply the proposed model to a real-life automotive supply chain case and demonstrate its superiority in providing uncertainty-immunized solutions. Our analysis reveals that the RCEP agreement may deepen the manufacturing supply chain networks in Asia–Pacific and promote their integration and localization. Also, we find that using any resilience strategy or a mixture of them can increase supply chain's resilient performance while decreasing costs against disruptions.