Screening and selection procedures with control variates and correlation induction techniques

We consider the problem of identifying the simulated system with the best expected performance measure when the number of alternatives is finite and small (often < 500). Recently, more research efforts in the simulation community have been directed to develop ranking and selection (R&S) procedures capable of exploiting variance reduction techniques (especially the control variates). In this article, we propose new R&S procedures that can jointly use control variates and correlation induction techniques (including antithetic variates and Latin hypercube sampling). Empirical results and a realistic illustration show that the proposed procedures outperform the conventional procedures using sample means or control variates alone.