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
On selecting the best individual in noisy environments
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Economic Analysis of Simulation Selection Problems
Management Science
Simulation optimization using the cross-entropy method with optimal computing budget allocation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Bayesian Simulation and Decision Analysis: An Expository Survey
Decision Analysis
Sequential Sampling to Myopically Maximize the Expected Value of Information
INFORMS Journal on Computing
Information Collection on a Graph
Operations Research
A brief introduction to optimization via simulation
Winter Simulation Conference
Do mean-based ranking and selection procedures consider systems' risk?
Winter Simulation Conference
The conjunction of the knowledge gradient and the economic approach to simulation selection
Winter Simulation Conference
Sequential Sampling with Economics of Selection Procedures
Management Science
A Framework for Selecting a Selection Procedure
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Ranking and selection meets robust optimization
Proceedings of the Winter Simulation Conference
Optimal computing budget allocation for small computing budgets
Proceedings of the Winter Simulation Conference
Ordinal optimization: a nonparametric framework
Proceedings of the Winter Simulation Conference
Performance measures for ranking and selection procedures
Proceedings of the Winter Simulation Conference
Rapid Screening Procedures for Zero-One Optimization via Simulation
INFORMS Journal on Computing
Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets
INFORMS Journal on Computing
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Selection procedures are used in a variety of applications to select the best of a finite set of alternatives. “Best” is defined with respect to the largest mean, but the mean is inferred with statistical sampling, as in simulation optimization. There are a wide variety of procedures, which begs the question of which selection procedure to select. The main contribution of this paper is to identify, through extensive experimentation, the most effective selection procedures when samples are independent and normally distributed. We also (a) summarize the main structural approaches to deriving selection procedures, (b) formalize new sampling allocations and stopping rules, (c) identify strengths and weaknesses of the procedures, (d) identify some theoretical links between them, and (e) present an innovative empirical test bed with the most extensive numerical comparison of selection procedures to date. The most efficient and easiest to control procedures allocate samples with a Bayesian model for uncertainty about the means and use new adaptive stopping rules proposed here.