A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
A combined procedure for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
An odds-ratio indifference-zone selection procedure for Bernoulli populations
WSC '04 Proceedings of the 36th conference on Winter simulation
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Statistical ranking and selection (R&S) is a collection of experiment design and analysis techniques for selecting the "population" with the largest or smallest mean performance from among a finite set of alternatives. R&S procedures have received considerable research attention in the stochastic simulation community, and they have been incorporated in commercial simulation software. One of the ways that R&S procedures are evaluated and compared is via the expected number of samples (often replications) that must be generated to reach a decision. In this paper we argue that sampling cost alone does not adequately characterize the efficiency of ranking-and-selection procedures, and we introduce a new sequential procedure that provides the same statistical guarantees as existing procedures while reducing the expected total cost of application.