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Solving Dial-a-Ride Problems with a Low-Level Hybridization of Ants and Constraint Programming

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Nature Inspired Problem-Solving Methods in Knowledge Engineering (IWINAC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4528))

Abstract

This paper is about Set Partitioning formulation and resolution for a particular case of VRP, the Dial-a-ride Problem. Set Partitioning has demonstrated to be useful modeling this problem and others very visible and economically significant problems. But the main disadvantage of this model is the need to explicitly generate a large set of possibilities to obtain good solutions. Additionally, in many cases a prohibitive time is needed to find the exact solution. Nowadays, many efficient metaheuristic methods have been developed to make possible a good solution in a reasonable amount of time. In this work we try to solve it with Low-level Hybridizations of Ant Colony Optimization and Constraint Programming techniques helping the construction phase of the ants. Computational results solving some benchmark instances are presented showing the advantages of using this kind of hybridization.

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Authors and Affiliations

  1. Pontificia Universidad Católica de Valparaíso, Chile

    Broderick Crawford

  2. Universidad Técnica Federico Santa María, Valparaíso, Chile

    Broderick Crawford, Carlos Castro & Eric Monfroy

  3. LINA, Université de Nantes, France

    Eric Monfroy

Authors
  1. Broderick Crawford
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  2. Carlos Castro
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  3. Eric Monfroy
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José Mira José R. Álvarez

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Crawford, B., Castro, C., Monfroy, E. (2007). Solving Dial-a-Ride Problems with a Low-Level Hybridization of Ants and Constraint Programming. In: Mira, J., Álvarez, J.R. (eds) Nature Inspired Problem-Solving Methods in Knowledge Engineering. IWINAC 2007. Lecture Notes in Computer Science, vol 4528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73055-2_34

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  • DOI: https://doi.org/10.1007/978-3-540-73055-2_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73054-5

  • Online ISBN: 978-3-540-73055-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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