Simulation-based optimization with surrogate models-Application to supply chain management

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. Several techniques are embedded in the framework to achieve the targeted objective: least square support vector machine (LSSVM), Bayesian evidence framework, and design and analysis of computer experiment (DACE). The extended framework is illustrated using two small examples and then applied to optimize the inventory levels in a three-stage supply chain. The results show that the framework identifies good solutions efficiently, can accommodate chance constraints and scales up well.