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
Large deviations perspective on ordinal optimization of heavy-tailed systems
Proceedings of the 40th Conference on Winter Simulation
Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets
INFORMS Journal on Computing
Stochastic resource allocation using a predictor-based heuristic for optimization via simulation
Computers and Operations Research
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We consider the problem of selecting the best system using simulation-based ordinal optimization. This problem has been studied mostly in the context of light-tailed distributions, where both Gaussian-based heuristics and asymptotically optimal procedures have been proposed. The latter rely on detailed knowledge of the underlying distributions and give rise to an exponential decay of the probability of selecting the incorrect system. However, their implementation tends to be computationally intensive. In contrast, in the presence of heavy tails the probability of selecting the incorrect system only decays polynomially, but this is achieved using simple allocation schemes that rely on little information of the underlying distributions. These observations are illustrated via several numerical experiments and are seen to be consistent with asymptotic theory.