;Ranking and Selection (R&S) is a kind of stochastic simulation for finding the system with best or near-best performance from among a finite number of alternatives. It also allows the experimenters to obtain results with a certain level of confidence. However, because of managerial or physical limits, sometimes we will face constraints on other performances. Therefore, Andradottir and Kim (2010) developed a Feasibility checking procedure (FCP) to find feasible or near-feasible systems which satisfied the stochastic constraints based on statistical theory. Nevertheless, the procedure can be inefficient when the number of candidate systems or the variances of sampling performances outputs are large. In this paper, we propose a new R&S procedure, combine the variance reduction techniques of Control variates (CV) with the FDP procedure. We provide a queuing example to compare our procedure with previous ones. In our procedure, we use a set of random variables that are correlated with the outputs of interest, whose means are known to the user, to replace the origin output. Since it can reduce the variance of the estimator for original, the new procedure is expected to be more efficient than other competitors in the sense that fewer observations and less computer time are needed to find the best system which under the constraints.
