Adapt selection procedures to process correlated and non-normal data with batch means

Many simulation output analysis procedures are derived based on the assumption that data are independent and identically distributed (i.i.d.) normal; examples include ranking and selection procedures and multiple-comparison procedures. The method of batch means is the technique of choice to ¿manufacture¿ data that are approximately i.i.d. normal when the raw samples are not. Batch means are sample means of subsets of consecutive subsamples from a simulation output sequence. We propose to incorporate the procedure of determining the batch size to obtain approximately i.i.d. normal batch means into the selection procedures of comparing the performance of alternative system designs. We performed an empirical study to evaluate the performance of the extended selection procedure.