Finding Feasible Systems in the Presence of a Probability Constraint

We consider the problem of determining feasible systems among a finite set of simulated alternatives with respect to a probability constraint, where observations from stochastic simulations are Bernoulli distributed. Most statistically valid procedures for feasibility determination consider constraints on the means of normally distributed observations. When observations are Bernoulli distributed, one can still use the existing procedures by treating batch means of Bernoulli observations as basic observations. However, achieving approximate normality may require a large batch size, which can lead to unnecessary waste of observations in reaching a decision. This paper proposes a procedure that utilizes Bernoulli-distributed observations to perform feasibility checks. We demonstrate that when the observations are Bernoulli distributed, our procedure outperforms an existing feasibility determination procedure that was developed for a constraint on normally distributed observations.