Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
An empirical evaluation of several methods to select the best system
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation with Arena
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Ranking and Selection for Steady-State Simulation: Procedures and Perspectives
INFORMS Journal on Computing
Corrigendum: New Selection Procedures
Operations Research
Using Ranking and Selection to "Clean Up" after Simulation Optimization
Operations Research
Selection Procedures with Frequentist Expected Opportunity Cost Bounds
Operations Research
Simulation selection problems: overview of an economic analysis
Proceedings of the 38th conference on Winter simulation
Performance evaluations of comparison-with-a-standard procedures
Proceedings of the 38th conference on Winter simulation
Simulated annealing in the presence of noise
Journal of Heuristics
The knowledge-gradient stopping rule for ranking and selection
Proceedings of the 40th Conference on Winter Simulation
Integrating techniques from statistical ranking into evolutionary algorithms
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
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Selection procedures are used in many applications to select the best of a finite set of alternatives, as in discrete optimization with simulation. There are a wide variety of procedures, which begs the question of which selection procedure to select. This paper (a) summarizes the main structural approaches to deriving selection procedures, (b) describes an innovative empirical testbed, and (c) summarizes results from work in progress that provides the most exhaustive assessment 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 a new expected opportunity cost-based stopping rule.