An Indifference-zone Selection Procedure with Minimum Switching and Sequential Sampling

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.