Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
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
Approximations for Digital Computers
Approximations for Digital Computers
Using parallel and distributed computing to increase the capability of selection procedures
WSC '05 Proceedings of the 37th conference on Winter simulation
Performance evaluations of comparison-with-a-standard procedures
Proceedings of the 38th conference on Winter simulation
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
A multi-objective selection procedure of determining a Pareto set
Computers and Operations Research
Proceedings of the 40th Conference on Winter Simulation
An enhanced lognormal selection procedure
Discrete Event Dynamic Systems
ADAPT Selection procedures to process correlated and non-normal data with batch means
Winter Simulation Conference
Some insights of using common random numbers in selection procedures
Discrete Event Dynamic Systems
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Indifference-zone selection procedures have been widely studied and applied to determine the sample sizes for selecting a good design among k alternative designs. However, efficiency is still a key concern for using simulation to solve ranking and selection problems. Ordinal optimization has emerged as an effective technique to improve efficiency of simulation and optimization. In this paper, we incorporate the concept of ordinal optimization with ranking-and-selection methodology and propose using a normal approximation to estimate the probability of correct selection. The proposed procedure takes into account not only the sample variances but also the difference of sample means when determining the sample sizes. Furthermore, the procedure is valid with the variance reduction technique of common random numbers. An experimental performance evaluation demonstrates the efficiency of the new procedure.