Advances in Decision Sciences
Volume 2010 (2010), Article ID 948359, 12 pages
doi:10.1155/2010/948359
Research Article

A New Approach to Estimate the Critical Constant of Selection Procedures

E. Jack Chen1 and Min Li2

1Business Systems, BASF Corporation, 333 Mount Hope Avenue, Rockaway, NJ 07866-0909, USA
2College of Business Administration, California State University, Sacramento, 6000 J Street, Sacramento, CA 95819-6088, USA

Received 12 March 2009; Revised 26 September 2009; Accepted 8 January 2010

Academic Editor: Eric J. Beh

Copyright © 2010 E. Jack Chen and Min Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A solution to the ranking and selection problem of determining a subset of size m containing at least c of the v best from k normal distributions has been developed. The best distributions are those having, for example, (i) the smallest means, or (ii) the smallest variances. This paper reviews various applicable algorithms and supplies the operating constants needed to apply these solutions. The constants are computed using a histogram approximation algorithm and Monte Carlo integration.