Restricted subset selection procedures for simulation
Operations Research
Ranking, selection and multiple comparisons in computer simulations
WSC '94 Proceedings of the 26th conference on Winter simulation
Selecting the best system in steady-state simulations using batch means
WSC '95 Proceedings of the 27th conference on Winter simulation
A method for discrete stochastic optimization
Management Science
Selecting the best system in transient simulations with variances known
WSC '96 Proceedings of the 28th conference on Winter simulation
Optimization via adaptive sampling and regenerative simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Statistical selection of the best system
Proceedings of the 33nd conference on Winter simulation
Solution quality of random search methods for discrete stochastic optimization
Mathematics and Computers in Simulation
Application of multi-objective simulation-optimization techniques to inventory management problems
WSC '05 Proceedings of the 37th conference on Winter simulation
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Selecting the best simulated system with weighted control-variate estimators
Mathematics and Computers in Simulation
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We consider the problem of selecting the stochastic system with the best expected performance measure, when the number of alternative systems is large. We consider the case of discrete event dynamic systems (DEDS) where the standard clock simulation technique can be used for simulating multiple systems using only one sample path. In this paper, we use a two-phase procedure that uses the standard clock simulation technique. In the first phase, we screen out non-competent alternatives and construct a confidence set that contains the best alternative with a pre-specified large probability. In the second phase, we use the indifference-zone ranking and selection procedure to select the best expected alternative among the survivals of the first phase. We implement this algorithm for solving a practical simulation optimization problem.