期刊
INFORMS JOURNAL ON COMPUTING
卷 25, 期 3, 页码 527-542出版社
INFORMS
DOI: 10.1287/ijoc.1120.0519
关键词
constrained simulation optimization; optimal allocation; ranking and selection
资金
- Office of Naval Research [N000140810066, N000140910997, N000141110065]
- National Science Foundation [CMMI 0758441, CMMI 0800688]
Consider the context of selecting an optimal system from among a finite set of competing systems, based on a stochastic objective function and subject to multiple stochastic constraints. In this context, we characterize the asymptotically optimal sample allocation that maximizes the rate at which the probability of false selection tends to zero. Since the optimal allocation is the result of a concave maximization problem, its solution is particularly easy to obtain in contexts where the underlying distributions are known or can be assumed. We provide a consistent estimator for the optimal allocation and a corresponding sequential algorithm fit for implementation. Various numerical examples demonstrate how the proposed allocation differs from competing algorithms.
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