Optimal Computing Budget Allocation for Complete Ranking

Previous research in ranking and selection focused on selecting the best design and subset selection. Little research has been done for ranking all designs completely. Complete ranking has been applied to design of experiment, random number generator and population-based search algorithms. In this paper, we consider the problem of ranking all designs. Our objective is to develop an efficient simulation allocation procedure that maximizes the probability of correct ranking with fixed limited computing budget. A previous allocation strategy of complete ranking based on indifference zone formulation is conservative and not efficient enough. We use the optimal computing budget allocation framework to further enhance the efficiency and reduce the amount of budget needed to achieve the same probability of correct ranking. Compared with the previous allocation strategy, our proposed allocation rule performs best under different scenarios.