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An Interactive Multiobjective Programming Approach to Combinatorial Data Analysis

Published online by Cambridge University Press:  01 January 2025

Michael J. Brusco  and
Stephanie Stahl
Michael J. Brusco*
Affiliation:
Information & Management Sciences, Florida State University
Stephanie Stahl
Affiliation:
Tallahassee, Florida
*
Requests for reprints should be sent to Michael J. Brusco, IMS Department, Florida State University, Tallahassee, FL 32306-1110. E-Mail: mbrusco@cob.fsu.edu
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Abstract

Combinatorial optimization problems in the social and behavioral sciences are frequently associated with a variety of alternative objective criteria. Multiobjective programming is an operations research methodology that enables the quantitative analyst to investigate tradeoffs among relevant objective criteria. In this paper, we describe an interactive procedure for multiobjective asymmetric unidimensional seriation problems. This procedure uses a dynamic-programming algorithm to partially generate the efficient set of sequences for small to medium-sized problems, and a multioperation heuristic to estimate the efficient set for larger problems. The interactive multiobjective procedure is applied to an empirical data set from the psychometric literature. We conclude with a discussion of other potential areas of application in combinatorial data analysis.

Keywords

combinatorial optimizationmultiobjective programmingseriation

Information

Type
Articles
Information
Psychometrika , Volume 66 , Issue 1 , March 2001 , pp. 5 - 24
Copyright
Copyright © 2001 The Psychometric Society

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Footnotes

Stephanie Stahl is a freelance writer and editor. She can be reached via e-mail at s-stahl@worldnet.att.net.

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