Optimal computing budget allocation for constrained optimization

Authors:
Nugroho Artadi Pujowidianto;Loo Hay Lee;Chun-Hung Chen;Chee Meng Yap
Affiliations:
National University of Singapore, Kent Ridge Crescent, Singapore;National University of Singapore, Kent Ridge Crescent, Singapore;George Mason University, University Drive, Fairfax, Virginia;National University of Singapore, Kent Ridge Crescent, Singapore
Venue:
Winter Simulation Conference
Year:
2009

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Abstract

In this paper, we consider the problem of selecting the best design from a discrete number of alternatives in the presence of a stochastic constraint via simulation experiments. The best design is the design with smallest mean of main objective among the feasible designs. The feasible designs are the designs of which constraint measure is below the constraint limit. The Optimal Computing Budget Allocation (OCBA) framework is used to tackle the problem. In this framework, we aim at maximizing the probability of correct selection given a computing budget by controlling the number of simulation replications. An asymptotically optimal allocation rule is derived. A comparison with Equal Allocation (EA) in the numerical experiments shows that the proposed allocation rule gains higher probability of correct selection.