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New Procedures to Select the Best Simulated System Using Common Random Numbers

Department of Industrial and Operations Engineering, The University of Michigan, 1205 Beal Avenue, Ann Arbor, Michigan 48109-2117
Department of Industrial and Operations Engineering, The University of Michigan, 1205 Beal Avenue, Ann Arbor, Michigan 48109-2117
sechick{at}engin.umich.edu
sechick{at}engin.umich.edu

Although simulation is widely used to select the best of several alternative system designs, and common random numbers is an important tool for reducing the computation effort of simulation experiments, there are surprisingly few tools available to help a simulation practitioner select the best system when common random numbers are employed. This paper presents new two-stage procedures that use common random numbers to help identify the best simulated system. The procedures allow for screening and attempt to allocate additional replications to improve the value of information obtained during the second stage, rather than determining the number of replications required to provide a given probability of correct selection guarantee. The procedures allow decision makers to reduce either the expected opportunity cost associated with potentially selecting an inferior system, or the probability of incorrect selection. A small empirical study indicates that the new procedures outperform several procedures with respect to several criteria, and identifies potential areas for further improvement.

Key Words: Multiple Selection; Ranking and Selection; Discrete-Event Simulation; Common Random Numbers; Missing Data; Bayesian Statistics
History: Received: July 9, 1999;

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