The P2 algorithm for dynamic calculation of quantiles and histograms without storing observations
Communications of the ACM
A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Proceedings of the 35th conference on Winter simulation: driving innovation
Estimating steady-state distributions via simulation-generated histograms
Computers and Operations Research
Using quantiles in ranking and selection procedures
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Selecting a Selection Procedure
Management Science
Mean-variance based ranking and selection
Proceedings of the Winter Simulation Conference
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The legacy simulation approach in ranking and selection procedures compares systems based on a mean performance metric. The best system is most often deemed as the one with the largest (or smallest) mean performance metric. In this paper, we discuss the limitations of the mean-based selection approach. We explore other selection criterion and discuss new approaches based on stochastic dominance using an appropriate section of the distribution function of the performance metric. In this approach, the decision maker has the flexibility to determine a section of the distribution function based on the specific features of the selection problem representing either the downside risk, upside risk, or central tendency of the performance metric. We discuss two different ranking and selection procedures based on this new approach followed by a small experiment and present some open research problems.