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The Opportunity cost and OCBA selection procedures in ordinal optimization

Author: He, Donghai ; Chick, Stephen ; Chen, C. H.INSEAD Area: Technology and Operations ManagementIn: IEEE Transactions on Systems Man and Cybernetics - Part C, Part C, vol. 37, no. 5, September 2007 Language: EnglishDescription: p. 951-961.Type of document: INSEAD ArticleNote: Please ask us for this itemAbstract: 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 the 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
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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 the 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

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