A gradient approach for smartly allocating computing budget for discrete event simulation
WSC '96 Proceedings of the 28th conference on Winter simulation
New development of optimal computing budget allocation for discrete event simulation
Proceedings of the 29th conference on Winter simulation
Statistical screening, selection, and multiple comparison procedures in computer simulation
Proceedings of the 30th conference on Winter simulation
Sequential allocations that reduce risk for multiple comparisons
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
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Bayesian methods: bayesian methods for simulation
Proceedings of the 32nd conference on Winter simulation
New results on procedures that select the best system using CRN
Proceedings of the 32nd conference on Winter simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Statistical analysis of simulation output: output data analysis for simulations
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Bayesian methods for discrete event simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
Review of advanced methods for simulation output analysis
WSC '05 Proceedings of the 37th conference on Winter simulation
New developments in ranking and selection: an empirical comparison of the three main approaches
WSC '05 Proceedings of the 37th conference on Winter simulation
Selection Procedures with Frequentist Expected Opportunity Cost Bounds
Operations Research
On selecting the best individual in noisy environments
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Information Collection on a Graph
Operations Research
A selecting-the-best method for budgeted model selection
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
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Simulation is an important tool for comparing the performance of several alternative systems. There is therefore significant interest in procedures that efficiently select the best system, where best is defined by the maximum or minimum expected simulation output. In this paper, we examine both two-stage and sequential procedures that represent three structurally different modeling methodologies for allocating simulation replications to identify the best system, and we evaluate them empirically with respect to several measures of effectiveness. Empirical evidence suggests that sequential procedures perform better than their two-stage counterparts, including a heuristic sequential variation on Rinott's procedure. Further, there appears to be significant benefit to using procedures based on a Bayesian, average-case analysis as opposed to the statistically-conservative indifference-zone formulation.