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• W2142858180 abstract "<para xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> 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. </para>" @default.
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• W2142858180 date "2007-09-01" @default.
• W2142858180 modified "2023-10-10" @default.
• W2142858180 title "Opportunity Cost and OCBA Selection Procedures in Ordinal Optimization for a Fixed Number of Alternative Systems" @default.
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• W2142858180 doi "https://doi.org/10.1109/tsmcc.2007.900656" @default.
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