Technology Management Area

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 of [2], 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