A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A combined procedure for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Using Ranking and Selection to "Clean Up" after Simulation Optimization
Operations Research
Finding probably better system configurations quickly
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
Best-subset selection procedure
Proceedings of the Winter Simulation Conference
A minimal switching procedure for constrained ranking and selection
Proceedings of the Winter Simulation Conference
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We propose an indifference-zone approach for a ranking and selection problem with the goal of reducing both the number of simulated samples of the performance and the frequency of configuration changes. We prove that with a prespecified high probability, our algorithm finds the best system configuration. Our proof hinges on several ideas, including the use of Anderson's probability bound, that have not been fully investigated for the ranking and selection problem. Numerical experiments show that our algorithm can select the best system configuration using up to 50% fewer simulated samples than existing algorithms without increasing the frequency of configuration changes.