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• W2143242804 abstract "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." @default.
• W2143242804 created "2016-06-24" @default.
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• W2143242804 creator A5062066146 @default.
• W2143242804 date "2013-08-01" @default.
• W2143242804 modified "2023-10-12" @default.
• W2143242804 title "Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets" @default.
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• W2143242804 doi "https://doi.org/10.1287/ijoc.1120.0519" @default.
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