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Enhancing feasibility determination with kriging metamodels

Feasibility determination in a stochastic setting is to determine for each alternative design whether its performance, which can only be estimated through simulation, exceeds a known threshold. Several intelligent simulation budget allocation methods have been developed to enhance the simulation efficiency of feasibility determination. To further improve the simulation efficiency, we develop a new budget allocation method which takes into consideration the relationships between different designs. We apply kriging metamodels to capture the inter-design relationships, and incorporate the information obtained from kriging into our allocation procedure based on the Bayesian framework. Numerical experiments demonstrate the efficacy of the proposed approach.