Selecting the best system: a decision-theoretic approach
Proceedings of the 29th conference on Winter simulation
Batching methods for simulation output analysis: a stopping procedure based on phi-mixing conditions
Proceedings of the 32nd conference on Winter simulation
Comparing systems via stochastic simulation: an enhanced two-stage selection procedure
Proceedings of the 32nd conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Statistical selection of the best system
Proceedings of the 33nd conference on Winter simulation
Using common random numbers for indifference-zone selection
Proceedings of the 33nd conference on Winter simulation
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
Using Ordinal Optimization Approach to Improve Efficiency of Selection Procedures
Discrete Event Dynamic Systems
Indifference zone selection procedures: inferences from indifference-zone selection procedures
Proceedings of the 35th conference on Winter simulation: driving innovation
Enhancing evolutionary algorithms with statistical selection procedures for simulation optimization
WSC '05 Proceedings of the 37th conference on Winter simulation
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Two-stage selection procedures have been widely studied and applied in determining the required sample size (i.e., the number of replications or batches) for selecting the best of k designs. The Enhanced Two-Stage Selection (ETSS) procedure is a heuristic two-stage selection procedure that takes into account not only the variance of samples, but also the difference of sample means when determining the sample sizes. This paper discusses the use of a conservative adjustment with the ETSS procedure to increase the probability of correct selection. We show how the adjustment allocates more simulation replications or batches to more promising designs at the second stage. An experimental performance evaluation demonstrates the efficiency of the adjustment.