A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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
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
Recent advances in simulation optimization: a conservative adjustment to the ETSS procedure
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Using parallel and distributed computing to increase the capability of selection procedures
WSC '05 Proceedings of the 37th conference on Winter simulation
Enhancing evolutionary algorithms with statistical selection procedures for simulation optimization
WSC '05 Proceedings of the 37th conference on Winter simulation
Using quantiles in ranking and selection procedures
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Identifying Recommendable Products based on Signal Detection Theory
Proceedings of the 2010 conference on Bridging the Socio-technical Gap in Decision Support Systems: Challenges for the Next Decade
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Two-stage indifference-zone selection procedures have been widely studied and applied. It is known that most indifference-zone selection procedures also guarantee multiple comparisons with the best confidence intervals with half-width corresponding to the indifference amount. We provide the statistical analysis of multiple comparisons with a control confidence interval that bounds the difference between each design and the unknown best and multiple comparisons with the best confidence intervals. The efficiency of selection procedures can be improved by taking into consideration the differences of sample means, using the variance reduction technique of common random numbers, and using sequentialized selection procedures. An experimental performance evaluation demonstrates the validity of the confidence intervals and efficiency of sequentialized selection procedures.