Ranking and selection techniques with overlapping variance estimators for simulations

Some ranking and selection (R&S) procedures for steady-state simulation require estimates of the asymptotic variance parameter of each system to guarantee a certain probability of correct selection. In this paper, we show that the performance of such R&S procedures depends highly on the quality of the variance estimates that are used. In fact, we study the performance of R&S procedures using three new variance estimators-overlapping area, overlapping Cramér-von Mises, and overlapping modified jackknifed Durbin-Watson estimators-that show better long-run performance than other estimators previously used in conjunction with R&S procedures for steady-state simulations. © Taylor & Francis Group, LLC.