Ordinal Optimization with Computing Budget Allocation for Selecting an Optimal Subset

In this paper, we consider the problem of selecting the top m systems when the number of alternative systems is very large. We propose a sequential procedure that consists of two stages to solve this problem. The procedure is a combination of the ordinal optimization (OO) technique and optimal computing budget allocation (OCBA) method. In the first stage, the OO is used to select a subset that overlaps with the set of actual best k% systems with high probability. Then in the second stage the optimal computing budget is used to select the top m systems from the selected subset. The proposed procedure is tested on two numerical examples. The numerical tests show that the proposed procedure is able to select a subset of best systems with high probability and short simulation time.