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Computational Results for Four Exact Methods to Solve the Three-Objective Assignment Problem

In: Multiobjective Programming and Goal Programming

Author

Listed:
  • Przybylski Anthony

    (FRE CNRS 2729—Université de Nantes)

  • Gandibleux Xavier

    (FRE CNRS 2729—Université de Nantes)

  • Matthias Ehrgott

    (University of Auckland)

Abstract

Most of the published exact methods for solving multi-objective combinatorial optimization problems implicitely use properties of the bi-objective case and cannot easily be generalized to more than two objectives. Papers that deal ex-plicitely with three (or more) objectives are relatively rare and often recent. Very few experimental results are known for these methods and no comparison has been done. We have recently developed a generalization of the two phase method that we have applied to the three-objective assignment problem. In order to evaluate the performance of our method we have implemented three exact methods found in the literature. We provide an analysis of the performance of each method and explain the main difficulties observed in their application to the three-objective assignment problem.

Suggested Citation

  • Przybylski Anthony & Gandibleux Xavier & Matthias Ehrgott, 2009. "Computational Results for Four Exact Methods to Solve the Three-Objective Assignment Problem," Lecture Notes in Economics and Mathematical Systems, in: Vincent Barichard & Matthias Ehrgott & Xavier Gandibleux & Vincent T'Kindt (ed.), Multiobjective Programming and Goal Programming, pages 79-88, Springer.
  • Handle: RePEc:spr:lnechp:978-3-540-85646-7_8
    DOI: 10.1007/978-3-540-85646-7_8
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    Citations

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    Cited by:

    1. Kirlik, Gokhan & Sayın, Serpil, 2014. "A new algorithm for generating all nondominated solutions of multiobjective discrete optimization problems," European Journal of Operational Research, Elsevier, vol. 232(3), pages 479-488.
    2. Angelo Aliano Filho & Antonio Carlos Moretti & Margarida Vaz Pato & Washington Alves Oliveira, 2021. "An exact scalarization method with multiple reference points for bi-objective integer linear optimization problems," Annals of Operations Research, Springer, vol. 296(1), pages 35-69, January.
    3. Klamroth, Kathrin & Stiglmayr, Michael & Sudhoff, Julia, 2023. "Ordinal optimization through multi-objective reformulation," European Journal of Operational Research, Elsevier, vol. 311(2), pages 427-443.

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