Restricted subset selection procedures for simulation
Operations Research
Methods for selecting the best system
WSC '91 Proceedings of the 23rd conference on Winter simulation
A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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
Comparison with a standard via fully sequential procedures
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Procedures for feasibility detection in the presence of multiple constraints
WSC '05 Proceedings of the 37th conference on Winter simulation
Finding the best in the presence of a stochastic constraint
WSC '05 Proceedings of the 37th conference on Winter simulation
Finding probably best systems quickly via simulations
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Finding feasible systems in the presence of constraints on multiple performance measures
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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We propose an indifference-zone approach for a ranking and selection (R&S) problem with the goal of finding the best-subset from a finite number of competing simulated systems given a level of correct-selection probability. Here the "best" system refers to the system with the largest or smallest performance measures. We present a best-subset selection procedure that can effectively eliminate the non-competitive systems and return only those alternatives as the selection result where statistically confident conclusions hold. Numerical experiments document that our procedure works well by selecting the correct best-subset with very high probability.