Optimal computing budget allocation for ranking the top designs with stochastic constraints

Comparing with the well-studied unconstrained ranking and selecting problems in simulation, literatures on constrained ranking and selection problems are relatively fewer. In this paper, we consider the problem of ranking the top-m designs subjected to stochastic constraints, where the design performance of the main objective as well as the constraint measures can only be estimated from simulation. Using the optimal computing budget allocation framework, we derive an asymptotically optimal allocation rule. The effectiveness of the suggested rule is demonstrated via numerical experiments.