Ranking and Selection with Bounded Observations

Purpose/Significance Ranking and selection is a fundamental research problem in the area of simulation optimization. The problem aims to select the statistical population with the largest mean from a finite set of statistical populations by taking samples and observing the random outputs. In the existing literature, while designing procedures to solve the problem, one often assumes that the outputs follow normal distributions and develop procedures based on the statistical properties of normal random variables. However, in practice, the normality assumption on the outputs may sometimes fail to hold. Once the normality assumption is violated, a procedure’s finite-time statistical validity may no longer hold. Design/Methodology To overcome this issue, this paper focuses on solving the problem where the outputs are drawn from bounded distributions and develop a fully-sequential procedure. Conclusions/Findings A class of sequential elimination algorithms is designed to solve the ranking problem where the output is a random number with bounded domain, and numerical experiments verify that the efficiency of this paper is much higher than the existing SE, ME and lil′DCB algorithms.