A new strategy for selecting good enough designs using optimal computing budget allocation

In this paper, we consider the problem of selecting a subset of good designs from a finite set of simulated designs. Instead of selecting exactly the top r$$(r>1)$$(r>1) designs from k alternatives, our approach seeks to select r good enough designs which are defined as designs from the set of top g designs $$(r\le g < k)$$(r≤g<k). By doing so, the selection efficiency could be improved significantly, and the performances of the selected designs remain in an acceptable range. Using the optimal computing budget allocation framework, we formulate the problem as that of maximizing the probability of correctly selecting r good enough designs subject to a simulation budget constraint. Based on two different approximate measures of the probability of correct selection, we derive two asymptotically optimal selection procedures for selecting a good enough subset. Some computational experiments are conducted to compare the performance of the proposed allocation rules with other methods in the literature.