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Fast Approximation Heuristics for Multi-Objective Vehicle Routing Problems

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Applications of Evolutionary Computation (EvoApplications 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6025))

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Abstract

The article describes an investigation of the use of fast approximation heuristics for multi-objective vehicle routing problems (MO-VRP). We first present a constructive heuristic based on the savings approach, which we generalize to fit the particular multi-objective nature of the problem. Then, an iterative phase based on local search improves the solutions towards the Pareto-front. Experimental investigations on benchmark instances taken from the literature show that the required computational effort for approximating such problems heavily depends on the underlying structures of the data sets. The insights gained in our study are particularly valuable when giving recommendations on how to solve a particular MO-VRP or even a particular MO-VRP instance, e. g. by means of a posteriori or interactive optimization approaches.

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Author information

Authors and Affiliations

  1. Logistics Management Department, Helmut Schmidt University, University of the Federal Armed Forces Hamburg, Holstenhofweg 85, 22043, Hamburg, Germany

    Martin Josef Geiger

Authors
  1. Martin Josef Geiger
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, University of Strathclyde, 16 Richmond Street, G1 1XQ, Glasgow, UK

    Cecilia Di Chio

  2. School of Business, University College Dublin, Belfield, Dublin 4, UK

    Anthony Brabazon

  3. ’Dalla Molle’ Institute for Artificial Intelligence (IDSIA), Galleria 2, 6928, Manno-Lugano, Switzerland

    Gianni A. Di Caro

  4. Wilhelm Schickard-Institut für Informatik, Universität Tübingen, Sand 1, 72076, Tübingen, Germany

    Marc Ebner

  5. National University of Computer and Emerging Sciences (NUCES), A.K. Brohi Road, H-11/4, Islamabad, Pakistan

    Muddassar Farooq

  6. Fakultät für WiSo, HelmutSchmidt-Universität, Holstenhofweg 85, 22043, Hamburg, Germany

    Andreas Fink

  7. Department of Information Systems, Johannes Gutenberg-Universität, 55099, Mainz, Germany

    Jörn Grahl

  8. Mathematics and Computer Science Depatment, University of Richmond, 23173, Richmond, VA, USA

    Gary Greenfield

  9. Department of Informatics Engineering, Polo II - Pinhal de Marrocos, University of Coimbra, 3030 - 290, Coimbra, Portugal

    Penousal Machado

  10. School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, UK

    Michael O’Neill

  11. Istituto di Calcolo e Reti ad Alte Prestazioni (ICAR), Consiglio Nazionale delle Ricerche (CNR), Via P. Castellino 111, 80131, Naples, Italy

    Ernesto Tarantino

  12. School of Computing, Edinburgh Napier University, 10 Clinton Road, EH10 5DT, Edinburgh, UK

    Neil Urquhart

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Geiger, M.J. (2010). Fast Approximation Heuristics for Multi-Objective Vehicle Routing Problems. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6025. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12242-2_45

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  • DOI: https://doi.org/10.1007/978-3-642-12242-2_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12241-5

  • Online ISBN: 978-3-642-12242-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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