Selecting designs with the smallest variance of normal populations

Indifference–zone selection procedures have been used to select a design with the minimum or maximum expected performance measure among a finite number of simulated designs. While there have been significant advancements in selection methodologies, the majority of the selection procedures are developed to process the selection when the performance measures are the mean of some output. In this paper, we state and justify two sampling procedures for selecting a subset of size m that contains at least c of the v best of k independent normal populations, when the ranking parameters are the population variances. An experimental performance evaluation demonstrates the validity of these selection procedures.