Simple Procedures for Selecting the Best Simulated System When the Number of Alternatives is Large

Barry L. Nelson (), Julie Swann (), David Goldsman () and Wheyming Song ()
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Barry L. Nelson: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208-3119
Julie Swann: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
David Goldsman: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Wheyming Song: Department of Industrial Engineering, National Tsing Hua University, Hsinchu R.O.C., Taiwan

Operations Research, 2001, vol. 49, issue 6, 950-963

Abstract: In this paper, we address the problem of finding the simulated system with the best (maximum or minimum) expected performance when the number of alternatives is finite, but large enough that ranking-and-selection (R&S) procedures may require too much computation to be practical. Our approach is to use the data provided by the first stage of sampling in an R&S procedure to screen out alternatives that are not competitive, and thereby avoid the (typically much larger) second-stage sample for these systems. Our procedures represent a compromise between standard R&S procedures---which are easy to implement, but can be computationally inefficient---and fully sequential procedures---which can be statistically efficient, but are more difficult to implement and depend on more restrictive assumptions. We present a general theory for constructing combined screening and indifference-zone selection procedures, several specific procedures and a portion of an extensive empirical evaluation.

Keywords: Simulation; design of experiments: two-stage procedures; Simulation; statistical analysis: finding the best alternative; Statistics; design of experiments (search for similar items in EconPapers)
Date: 2001
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Citations: View citations in EconPapers (41)

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