A Multiple Attribute Utility Theory Approach to Ranking and Selection
Management Science
Ranking and selection with multiple "targets"
Proceedings of the 38th conference on Winter simulation
Recent advances in ranking and selection
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Selection of the best with stochastic constraints
Winter Simulation Conference
Optimal computing budget allocation for constrained optimization
Winter Simulation Conference
Best-subset selection procedure
Proceedings of the Winter Simulation Conference
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In this paper, we address the problem of finding a set of feasible or near-feasible systems among a finite number of simulated systems in the presence of stochastic constraints. Andradóttir, Goldsman, and Kim (2005) present a procedure that detects feasibility of systems in the presence of one constraint with a pre-specified probability of correctness. We extend their procedure to the case of multiple constraints by the use of the Bonferroni inequality. Unfortunately, the resulting procedure tends to be very conservative when the number of systems or constraints is large. As a remedy, we present a screening procedure that uses an aggregated observation, which is a linear combination of the collected observations across stochastic constraints. Then, we present an accelerated procedure that combine the extension of Andradóttir, Goldsman, and Kim (2005) with the procedure that uses aggregated observations. Some experimental results that compare the performance of the proposed procedures are presented.