Multiple Decision Procedures: Theory and Methodology of Selecting and Ranking Populations
Multiple Decision Procedures: Theory and Methodology of Selecting and Ranking Populations
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
A Multiple Attribute Utility Theory Approach to Ranking and Selection
Management Science
A large deviations perspective on ordinal optimization
WSC '04 Proceedings of the 36th conference on Winter simulation
Finding the best in the presence of a stochastic constraint
WSC '05 Proceedings of the 37th conference on Winter simulation
Proceedings of the 38th conference on Winter simulation
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Nested simulation for estimating portfolio losses within a time horizon
Winter Simulation Conference
Optimal computing budget allocation for constrained optimization
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
A minimal switching procedure for constrained ranking and selection
Proceedings of the Winter Simulation Conference
Efficient simulation budget allocation for selecting the best set of simplest good enough designs
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
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We consider the problem of feasibility determination in a stochastic setting. In particular, we wish to determine whether a system belongs to a given set Γ based on a performance measure estimated through Monte Carlo simulation. Our contribution is two-fold: (i) we characterize fractional allocations that are asymptotically optimal; and (ii) we provide an easily implementable algorithm, rooted in stochastic approximation theory, that results in sampling allocations that provably achieve in the limit the same performance as the optimal allocations. The finite-time behavior of the algorithm is also illustrated on two small examples.