A product family-based supply chain hypernetwork resilience optimization strategy

In product manufacturing, the supply chain can be adversely affected by the occurrence of uncertain risk events. Optimizing supply chain resilience is a challenging multi-objective nonlinear programming problem. Due to the development of mass customization production modes, manufacturing enterprises are more concerned with the service supply capability for a given product family. However, optimizing supply chain resilience while considering the product family is neglected in existing works. To tackle it, this paper first establishes a product family-based supply chain hypernetwork, mapping the product bill of materials to the potential suppliers. Second, a product family-based supply chain hypernetwork resilience optimization strategy (PF-SCHROS) is proposed using an improved genetic algorithm. Experiments are carried out to compare the proposed strategy with the traditional methods including interior point method, sequential quadratic programming, and genetic algorithm. Results show that our approach greatly reduces the loss originating from supplier chain disruption and can adequately improve supply chain service capability in various disruption scenarios.