On a paired-t confidence interval based ranking and selection method




Wu, Yifan
Liu, Liping
Li, Shu-Xia

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DEStech Publications, Inc.


Simulation optimization is a widely used methodology for complex stochastic system design. Though the simulation provides a flexible tool for model building, the optimization process could be very challenging due to the cumbersome computing times. A paired-t confidence interval based ranking and selection method is developed to tackle such a problem. The survive probability of each candidate system is calculated with the lower and upper bounds of the confidence interval obtained using paired-t comparison method. The proposed method is tested with several numerical examples. The experimental results suggest that the proposed method outperforms the other classical Ranking and Selection procedures.


Paper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio
Includes bibliographical references


Simulation methods, Mathematical optimization, Ranking and selection (Statistics)