Simulation based optimization of supply chains with a surrogate model

Simulation is widely used in the decision making processes associated with supply chain management. In this paper, we present an extension of the simulation based optimization framework which has been previously proposed for analyzing supply chains. The extension consists of the iterative construction of a surrogate model based on systematically accumulated simulation results to capture the causal relation between the key decision variables and supply chain performance. The decision variables can then be optimized using the surrogate model in place of individual simulation runs to economize on the overall computational effort. The extended framework is illustrated using a small example and then applied to optimize the inventory levels in a three stage supply chain.