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
Using parallel and distributed computing to increase the capability of selection procedures
WSC '05 Proceedings of the 37th conference on Winter simulation
Implications of heavy tails on simulation-based ordinal optimization
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
A new perspective on feasibility determination
Proceedings of the 40th Conference on Winter Simulation
A large deviations view of asymptotic efficiency for simulation estimators
Proceedings of the 40th Conference on Winter Simulation
Large deviations perspective on ordinal optimization of heavy-tailed systems
Proceedings of the 40th Conference on Winter Simulation
Hybrid algorithm for discrete event simulation based supply chain optimization
Expert Systems with Applications: An International Journal
Simulation optimization using the cross-entropy method with optimal computing budget allocation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Sequential Sampling to Myopically Maximize the Expected Value of Information
INFORMS Journal on Computing
Nested simulation for estimating portfolio losses within a time horizon
Winter Simulation Conference
Consistency of Sequential Bayesian Sampling Policies
SIAM Journal on Control and Optimization
Proceedings of the Winter Simulation Conference
Ordinal optimization: a nonparametric framework
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
Large-deviation sampling laws for constrained simulation optimization on finite sets
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
Asymptotic Simulation Efficiency Based on Large Deviations
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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 optimal allocation of computing budget to maximize the probability of correct selection in the ordinal optimization setting. This problem has been studied in the literature in an approximate mathematical framework under the assumption that the underlying random variables have a Gaussian distribution. We use the large deviations theory to develop a mathematically rigorous framework for determining the optimal allocation of computing resources even when the underlying variables have general, non-Gaussian distributions. Further, in a simple setting we show that when there exists an indifference zone, quick stopping rules may be developed that exploit the exponential decay rates of the probability of false selection. In practice, the distributions of the underlying variables are estimated from generated samples leading to performance degradation due to estimation errors. On a positive note, we show that the corresponding estimates of optimal allocations converge to their true values as the number of samples used for estimation increases to infinity.