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Using Ranking and Selection to “Clean Up” after Simulation Optimization

Justin Boesel¹; Barry L. Nelson²; Seong-Hee Kim³

¹ The MITRE Corporation, 1820 Dolley Madison Boulevard, McLean, Virginia 22102 · ² Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208-3119 · ³ School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332

In this paper we address the problem of finding the simulated system with the best (maximum or minimum) expected performance when the number of systems is large and initial samples from each system have already been taken. This problem may be encountered when a heuristic search procedure—perhaps one originally designed for use in a deterministic environment—has been applied in a simulation-optimization context. Because of stochastic variation, the system with the best sample mean at the end of the search procedure may not coincide with the true best system encountered during the search. This paper develops statistical procedures that return the best system encountered by the search (or one near the best) with a prespecified probability. We approach this problem using combinations of statistical subset selection and indifference-zone ranking procedures. The subset-selection procedures, which use only the data already collected, screen out the obviously inferior systems, while the indifference-zone procedures, which require additional simulation effort, distinguish the best from the less obviously inferior systems.

DOI

10.1287/opre.51.5.814.16751

Volume

51 (5)

Pages

814-825

Language

en

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BibTeX

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