Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Convex Optimization
Simulation optimization: simulation optimization
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
A large deviations perspective on ordinal optimization
WSC '04 Proceedings of the 36th conference on Winter simulation
A testbed of simulation-optimization problems
Proceedings of the 38th 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
A new perspective on feasibility determination
Proceedings of the 40th Conference on Winter Simulation
Selecting a Selection Procedure
Management Science
Finding feasible systems in the presence of constraints on multiple performance measures
ACM Transactions on Modeling and Computer Simulation (TOMACS)
The stochastic root-finding problem: Overview, solutions, and open questions
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Introduction to Rare Event Simulation
Introduction to Rare Event Simulation
SimOpt: a library of simulation optimization problems
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
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Consider the context of selecting an optimal system from among a finite set of competing systems, based on a “stochastic” objective function and subject to multiple “stochastic” constraints. In this context, we characterize the asymptotically optimal sample allocation that maximizes the rate at which the probability of false selection tends to zero. Since the optimal allocation is the result of a concave maximization problem, its solution is particularly easy to obtain in contexts where the underlying distributions are known or can be assumed. We provide a consistent estimator for the optimal allocation and a corresponding sequential algorithm fit for implementation. Various numerical examples demonstrate how the proposed allocation differs from competing algorithms.