A comparison between the OCBA and the KN selection procedures for maximizing the probability of correct selection

Abstract

Selecting a system or a subset of systems that have the best performance among many alternative systems is an important area of research. All efforts are paid to do this selection with a high probability of correct selection [Formula: see text]. In this paper, we compare the performance of two popular procedures used to select the best systems, namely, the method of Kim and Nelson [Formula: see text] which is considered as a ranking and selection procedure, and the Optimal Computing Budget Allocation [Formula: see text] procedure. Both of these procedures determine the number of samples for each system that are needed to achieve the maximum [Formula: see text]. Recently, the [Formula: see text] has been developed in order to be able to approximate the value of [Formula: see text], so both procedures approximate the value of [Formula: see text]. To do the comparison, we implement the two methods on two examples; one is a generic example and the other one involves a management of inventory model. The two examples assure that the [Formula: see text] method always outperforms the [Formula: see text] method.