Abstract
In this paper, we consider the problem of selecting an optimal subset from a finite set of simulated designs. Using the optimal computing budget allocation (OCBA) framework, we formulate the problem as that of maximizing the probability of correctly selecting the top m designs subject to a constraint on the total number of samples available. For an approximation of the probability of correct selection, we derive an asymptotically optimal subset selection procedure that is easy to implement. More importantly, we provide some useful insights on characterizing an efficient subset selection rule and how it can be achieved by adjusting the budgets allocated to the optimal and non-optimal subsets.