Ranking and selection (R&S) procedures have been considered an effective tool to solve simulation optimization problems with a discrete and finite decision space. Control variate (CV) is a variance reduction technique that requires no intervention in the way the simulation experiment is performed, and the least-squares regression package needed to implement CV is readily available. In this paper we propose two provably valid selection procedures that employ weighted CV estimators in different ways. Both procedures are guaranteed to select the best system with a prespecified confidence level. Empirical results and simple analyses are performed to compare the efficiency of our new procedures with some existing procedures.
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