WSC '93 Proceedings of the 25th conference on Winter simulation
A survey of ranking, selection, and multiple comparison procedures for discrete-event simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Batch-size effects on simulation optimization using multiple comparisons with the best
WSC' 90 Proceedings of the 22nd conference on Winter simulation
Simulation optimization: a survey of simulation optimization techniques and procedures
Proceedings of the 32nd conference on Winter simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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This paper considers the problem of determining the best of a finite number of system designs by simulation experimentation when the criterion of interest is maximum or minimum expected performance. This is a special case of the general problem of optimization via simulation. The proposed method is based on multiple comparisons with the best (MCB), due to Hsu, which constructs simultaneous interval estimates for the difference between the expected performance of each system design and the best of the other designs. We propose a refinement of Hsu's procedure through the use of two variance reduction techniques, common random numbers and control variates, that are particularly useful in simulation experiments. We show that the proposed procedure is better than standard MCB in the sense that it is more sensitive to differences in expected performance.