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This article introduces a new method for discrete decision variable optimization via simulation that combines the nested partitions method and the stochastic branch-and-bound method in the sense that advantage is taken of the partitioning structure of stochastic branch-and-bound, but the bounds are estimated based on the performance of sampled solutions, similar to the nested partitions method. The proposed Empirical Stochastic Branch-and-Bound (ESB&B) algorithm also uses improvement bounds to guide solution sampling for better performance. A convergence proof and empirical evaluation are provided. [Supplementary materials are available for this article. Go to the publisher’s online edition ofIIE Transactionfor datasets, additional tables, detailed proofs, etc.] [ABSTRACT FROM AUTHOR]

Fully sequential selection procedures have been developed in the field of stochastic simulation to find the simulated system with the best expected performance when the number of alternatives is finite. Kim and Nelson proposed the [image omitted] procedure to allow for unknown and unequal variances and the use of common random numbers. [image omitted] approximates the raw sum of differences between observations from two systems as a Brownian motion process with drift and uses a triangular continuation region to decide the stopping time of the selection process. In this paper new fully sequential selection procedures are derived that employ a more effective sum of differences, which is called a controlled sum. Two provably valid procedures and an approximate procedure are described. Empirical results and a realistic illustration are provided to compare the efficiency of these procedures with other procedures that solve the same problem. [Supplemental materials are available for this article. Go to the publisher's online edition of IIE Transactions for the following supplemental resources: Proofs and guidelines to choose appropriate parameters.] [ABSTRACT FROM AUTHOR]

This paper proposes a technique for estimating steady-state quantiles from discrete-event simulation models, with particular attention paid to cycle time quantiles of manufacturing systems. The technique is based on the Cornish-Fisher expansion, justified through an extensive empirical study, and is supported with mathematical analysis. It is shown that the technique provides precise and accurate estimates for the most commonly estimated quantiles with minimal data storage and low computational requirements. [ABSTRACT FROM AUTHOR]

Statistical Ranking and Selection (R&S) is a collection of experiment design and analysis techniques for selecting the system 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. All existing procedures assume that the set of alternatives is available at the beginning of the experiment. In many situations, however, the alternatives are revealed (generated) sequentially during the experiment. We introduce procedures that are capable of selecting the best alternative in these situations and provide the desired statistical guarantees. [ABSTRACT FROM AUTHOR]

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 the cost of switching among the simulations of the alternative systems should also be considered. We introduce two new, adaptive procedures, the minimum switching sequential procedure and the multi-stage sequential procedure with tradeoff, that provide the same statistical guarantees as existing procedures and significantly reduce the expected total computational cost of application, especially when applied to favorable configurations of the competing means. [ABSTRACT FROM AUTHOR]