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
A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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
Proceedings of the 35th conference on Winter simulation: driving innovation
Special topics on simulation analysis: better-than-optimal simulation run allocation?
Proceedings of the 35th conference on Winter simulation: driving innovation
Efficient simulation procedures: comparison with a standard via fully sequential procedures
Proceedings of the 35th conference on Winter simulation: driving innovation
Comparison with a standard via fully sequential procedures
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Bayesian methods for discrete event simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
Simulation optimization: a review, new developments, and applications
WSC '05 Proceedings of the 37th conference on Winter simulation
Review of advanced methods for simulation output analysis
WSC '05 Proceedings of the 37th conference on Winter simulation
Finding the best in the presence of a stochastic constraint
WSC '05 Proceedings of the 37th conference on Winter simulation
Bayesian ideas and discrete event simulation: why, what and how
Proceedings of the 38th conference on Winter simulation
Simulation Allocation for Determining the Best Design in the Presence of Correlated Sampling
INFORMS Journal on Computing
State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments
INFORMS Journal on Computing
Selection Procedures with Frequentist Expected Opportunity Cost Bounds
Operations Research
A tournament framework for the ranking and selection problem
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Extension of the direct optimization algorithm for noisy functions
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Some topics for simulation optimization
Proceedings of the 40th Conference on Winter Simulation
The knowledge-gradient stopping rule for ranking and selection
Proceedings of the 40th Conference on Winter Simulation
Finding probably best systems quickly via simulations
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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
Information Collection on a Graph
Operations Research
Simulation optimization with hybrid golden region search
Winter Simulation Conference
The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery
INFORMS Journal on Computing
A Framework for Selecting a Selection Procedure
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Ranking and selection with unknown correlation structures
Proceedings of the Winter Simulation Conference
Value of information methods for pairwise sampling with correlations
Proceedings of the Winter Simulation Conference
May the best man win: simulation optimization for match-making in e-sports
Proceedings of the Winter Simulation Conference
Guessing preferences: a new approach to multi-attribute ranking and selection
Proceedings of the Winter Simulation Conference
Hi-index | 0.01 |
Although simulation is widely used to select the best of several alternative system designs, and common random numbers is an important tool for reducing the computation effort of simulation experiments, there are surprisingly few tools available to help a simulation practitioner select the best system when common random numbers are employed. This paper presents new two-stage procedures that use common random numbers to help identify the best simulated system. The procedures allow for screening and attempt to allocate additional replications to improve the value of information obtained during the second stage, rather than determining the number of replications required to provide a given probability of correct selection guarantee. The procedures allow decision makers to reduce either the expected opportunity cost associated with potentially selecting an inferior system, or the probability of incorrect selection. A small empirical study indicates that the new procedures outperform several procedures with respect to several criteria, and identifies potential areas for further improvement.