Finding Feasible Systems for a Stochastic Constraint with Relaxed Tolerance Levels

We consider the problem of finding feasible systems with respect to a stochastic constraint when the performance of each system needs to be evaluated via simulation. We develop a new procedure, referred to as the Indifference-Zone Relaxation procedure, to lessen inefficiencies of existing procedures derived under the assumption that all systems are exactly the tolerance level away from the threshold. Specifically, our procedure introduces a set of relaxed tolerance levels and simultaneously implements two subroutines for each relaxed tolerance level: one to identify clearly feasible systems and the other to exclude clearly infeasible systems. As a result, the proposed procedure allows early determination of feasibility for some systems, while maintaining the statistical guarantee. The efficiency of the procedure is investigated through experimental results.