Journal of Multi-Criteria Decision Analysis

Volume 24, Issue 1-2
RESEARCH ARTICLE

Easy to say they are Hard, but Hard to see they are Easy— Towards a Categorization of Tractable Multiobjective Combinatorial Optimization Problems

José Rui Figueira

CEG‐IST Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

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Carlos M. Fonseca

CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

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Pascal Halffmann

Mathematical Institute, University of Koblenz‐Landau, Koblenz, Germany

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Kathrin Klamroth

School of Mathematics and Natural Sciences, University of Wuppertal, Wuppertal, Germany

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Luís Paquete

CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

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Stefan Ruzika

Mathematical Institute, University of Koblenz‐Landau, Koblenz, Germany

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Britta Schulze

School of Mathematics and Natural Sciences, University of Wuppertal, Wuppertal, Germany

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Michael Stiglmayr

Corresponding Author

E-mail address: stiglmayr@math.uni‐wuppertal.de

School of Mathematics and Natural Sciences, University of Wuppertal, Wuppertal, Germany

Correspondence to: Michael Stiglmayr, School of Mathematics and Natural Sciences, University of Wuppertal, Gaußstraße 20, 42119 Wuppertal, Germany. E‐mail: stiglmayr@math.uni‐wuppertal.de

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David Willems

Mathematical Institute, University of Koblenz‐Landau, Koblenz, Germany

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First published: 26 September 2016
Cited by: 3

Abstract

Multiobjective combinatorial optimization problems are known to be hard problems for two reasons: their decision versions are often NP‐complete, and they are often intractable. Apart from this general observation, are there also variants or cases of multiobjective combinatorial optimization problems that are easy and, if so, what causes them to be easy? This article is a first attempt to provide an answer to these two questions. Thereby, a systematic description of reasons for easiness is envisaged rather than a mere collection of special cases. In particular, the borderline of easy and hard multiobjective optimization problems is explored. Copyright © 2016 John Wiley & Sons, Ltd.

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