Two-stage stopping procedures based on standardized time series
Management Science
Convergence properties of ordinal comparison in the simulation of discrete event dynamic systems
Journal of Optimization Theory and Applications
Statistical selection of the best system
Proceedings of the 33nd conference on Winter simulation
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
Selecting the best system: selecting the best system: theory and methods
Proceedings of the 35th conference on Winter simulation: driving innovation
A large deviations perspective on ordinal optimization
WSC '04 Proceedings of the 36th conference on Winter simulation
Selecting a Selection Procedure
Management Science
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Simulation-based ordinal optimization has frequently relied on large deviations analysis as a theoretical device for arguing that it is computationally easier to identify the best system out of d alternatives than to estimate the actual performance of a given design. In this paper, we argue that practical implementation of these large deviations-based methods need to estimate the underlying large deviations rate functions of the competing designs from the samples generated. Because such rate functions are difficult to estimate accurately (due to the heavy tails that naturally arise in this setting), the probability of mis-estimation will generally dominate the underlying large deviations probability, making it difficult to build reliable algorithms that are supported theoretically through large deviations analysis. However, when we justify ordinal optimization algorithms on the basis of guaranteed finite sample bounds (as can be done when the associated random variables are bounded), we show that satisfactory and practically implementable algorithms can be designed.