Simulation based optimization for supply chain configuration design

The design of a supply chain network as an integrated system with several tiers of suppliers is a difficult task. It consists of making strategic decisions on the facility location, stocking location, production policy, production capacity, distribution and transportation modes. This research develops a hybrid optimization approach to address the supply chain configuration design problem. The new approach combines simulation, mixed integer programming and genetic algorithm. The genetic algorithm provides a mechanism to optimize qualitative and policy variables. The mixed integer programming model reduces computing efforts by manipulating quantitative variables. Finally simulation is used to evaluate performance of each supply chain configuration with nonlinear, complex relationships and under more realistic assumptions. The approach is designed to be robust and could handle the large scale of the real world problems.