A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Comparison with a Standard via All-Pairwise Comparisons
Discrete Event Dynamic Systems
Estimation of percentiles of cycle time in manufacturing simulation
WSC '05 Proceedings of the 37th conference on Winter simulation
Using parallel and distributed computing to increase the capability of selection procedures
WSC '05 Proceedings of the 37th conference on Winter simulation
Indirect cycle-time quantile estimation for non-FIFO dispatching policies
Proceedings of the 38th conference on Winter simulation
Estimating steady-state distributions via simulation-generated histograms
Computers and Operations Research
Single-stage multiple-comparison procedure for quantiles and other parameters
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Indifference-zone subset selection procedures: using sample means to improve efficiency
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Using quantiles in ranking and selection procedures
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
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Indifference-zone selection procedures have been used to select a design with the minimum or maximum expected performance measure among a finite number of simulated designs. While there have been significant advancements in selection methodologies, the majority of the selection procedures are developed to process the selection when the performance measures are the mean of some output. In some situations, quantiles provide more suitable information. Quantiles are also more robust to outliers than the mean and standard deviation. Moreover, selection procedures are often derived based on the assumption that the input data are independent and identically distributed (i.i.d.) normal. In this paper we state and justify selection procedures when the ranking parameter is quantile. It is our intention for the quantile estimates to play the role of the i.i.d. normal observations that the original versions of selection procedures process. That is, we assume that our quantile estimates are approximately i.i.d. normal. We perform an empirical study of several stochastic processes to evaluate the performance of the procedure.