Statistical screening, selection, and multiple comparison procedures in computer simulation
Proceedings of the 30th conference on Winter simulation
Comparison of Bayesian and frequentist assessments of uncertainty for selecting the best system
Proceedings of the 30th conference on Winter simulation
A Bonferroni selection procedure when using commom random numbers with unknown variances
WSC '86 Proceedings of the 18th conference on Winter simulation
An asymptotic allocation for simultaneous simulation experiments
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
A decision-theoretic approach to screening and selection with common random numbers
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
An empirical evaluation of several methods to select the best system
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation Modeling and Analysis
Simulation Modeling and Analysis
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Using common random numbers for indifference-zone selection
Proceedings of the 33nd conference on Winter simulation
Simulation optimization: simulation optimization
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Optimization via simulation: two-stage NP method with inheritance
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
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One use of simulation is to inform decision makers that seek to select the best of several alternative systems. The system with the highest (or lowest) mean value for simulation output is often selected as best, and simulation output is used to infer the value of the unknown mean of each system. Statistical procedures that help to identify the best system by suggesting an appropriate number of replications for each system are therefore useful tools in simulation. This article explores the performance of representative procedures from two approaches to develop statistical procedures, with the goal of understanding tradeoffs involving the ease of use, computational requirements, and the range of applicability. The focus is primarily on procedures that use common random numbers to sharpen comparisons between systems.