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Opportunity Cost and OCBA Selection Procedures in Ordinal Optimization for a Fixed Number of Alternative Systems

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3 Author(s)
Donghai He ; George Mason Univ., Fairfax ; Stephen E. Chick ; Chun-Hung Chen

Ordinal optimization offers an efficient approach for simulation optimization by focusing on ranking and selecting a finite set of good alternatives. Because simulation replications only give estimates of the performance of each alternative, there is a potential for incorrect selection. Two measures of selection quality are the alignment probability or the probability of correct selection (P{CS}), and the expected opportunity cost E[OC], of a potentially incorrect selection. Traditional ordinal optimization approaches focus on the former case. This paper extends Chen's optimal computing budget allocation (OCBA) approach, which allocated replications to improve P{CS}, to provide the first OCBA-like procedure that optimizes E[OC] in some sense. The procedure performs efficiently in numerical experiments.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)  (Volume:37 ,  Issue: 5 )