Procedures for feasibility detection in the presence of multiple constraints

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. Andradottir, Goldsman, and Kim (2005) present a procedure that detects feasibility of systems in the presence of one constraint with a prespecified 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 Andradottir, Goldsman, and Kim (2005) with the procedure that uses aggregated observations. Some experimental results that compare the performance of the proposed procedures are presented