A Multiple Attribute Utility Theory Approach to Ranking and Selection

John Butler (), Douglas J. Morrice () and Peter W. Mullarkey ()
Additional contact information
John Butler: Center for Information Technology Management, and Department of Accounting and MIS, The Ohio State University, Columbus, Ohio 43210
Douglas J. Morrice: Department of Management Science and Information Systems, CBA 5.202, The University of Texas at Austin, Austin, Texas 78712-1175
Peter W. Mullarkey: Maxager Technology, Inc., 2173 E. Francisco Boulevard, Suite C, San Rafael, California 94901

Management Science, 2001, vol. 47, issue 6, 800-816

Abstract: Managers of large industrial projects often measure performance by multiple attributes. For example, our paper is motivated by the simulation of a large industrial project called a land seismic survey, in which project performance is based on duration, cost, and resource utilization. To address these types of problems, we develop a ranking and selection procedure for making comparisons of systems (e.g., project configurations) that have multiple performance measures. The procedure combines multiple attribute utility theory with statistical ranking and selection to select the best configuration from a set of possible configurations using the indifference-zone approach. We apply our procedure to results generated by the simulator for a land seismic survey that has six performance measures, and describe a particular type of sensitivity analysis that can be used as a robustness check.

Keywords: Simulation; Ranking and Selection; Multiple Attribute Utility Theory (search for similar items in EconPapers)
Date: 2001
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (32)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.47.6.800.9812 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:47:y:2001:i:6:p:800-816

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().