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ABSTRACT

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.

REFERENCES

• 1: Andradóttir, S., D. Goldsman, and S.-H. Kim. 2005. Fully sequential procedures for comparing constrained systems via simulation. To appear in Proceedings of the 2005 Winter Simulation Conference, ed. M. E. Kuhl, N. M. Steiger, F. B. Armstrong, and J. A. Joines. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey.
• 2: Batur, D. and S.-H. Kim. 2005. Finding a set of feasible systems when the number of systems or constraints is large. Working paper, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia.
• 3: John Butler , Douglas J. Morrice , Peter W. Mullarkey, A Multiple Attribute Utility Theory Approach to Ranking and Selection, Management Science, v.47 n.6, p.800-816, June 2001 [doi>10.1287/mnsc.47.6.800.9812]
• 4: Santner, T. J., and A. C. Tamhane. 1984. Designing experiments for selecting a normal population with a large mean and a small variance. In Design of Experiments --- Ranking and Selection: Essays in Honor of Robert E. Bechhofer, ed. T. J. Santner and A. C. Tamhane, 179--198. New York: Marcel-Dekker.