Optimal sampling in design of experiment for simulation-based stochastic optimization

Simulation can be a very powerful tool to help decision making in many applications such as semiconductor manufacturing or healthcare, but exploring multiple courses of actions can be time consuming. We propose an optimal computing budget allocation (OCBA) method to improve the efficiency of simulation optimization using parametric regression. The approach proposed here, called OCBA-DOE, incorporates information from across the domain into a regression equation in order to efficiently allocate the simulation replications to improve the decision process. Asymptotic convergence rates of the OCBA-DOE method indicate that it offers a significant improvement when compared to a naive allocation scheme and the traditional OCBA method. Numerical experiments reinforce these results.

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