Ranking and selection procedures for simulation

We present sequential ranking and selection statistical procedures that determine the best simulated model configuration among competing alternatives. The best in this context denotes the largest expected value of a given performance metric. In order to run the procedures efficiently, we give algorithms using batched observations, which under certain conditions, exhibit the characteristics necessary for the appropriate application of ranking and selection procedures. We present empirical results that indicate that the sequential procedures are quite parsimonious, in terms of the number of required observations.