Finding the best in the presence of stochastic constraints

Our problem is that of finding the best system—i.e., the system with the largest or smallest primary perfor-mance measure—among a finite number of simulated systems in the presence of a stochastic constraint on a secondary performance measure. In order to solve this problem, we first find a set that contains feasible or near-feasible systems (Phase I) and then choose the best among those systems in the set (Phase II). We present a procedure for Phase I; and then we propose another procedure that performs Phases I and II se-quentially to find the best feasible system.