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An Evolutionary Variable Neighborhood Descent for Addressing an Electric VRP Variant

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Variable Neighborhood Search (ICVNS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11328))

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Abstract

Variable neighborhood searches and evolutionary techniques have shown their effectiveness when dealing with many combinatorial optimisation problems. This study proposes to combine these two techniques for addressing the routing problem using electric and modular vehicles. This is a recent problem that aims to overcome recharging battery constraints while maintaining a certain performance regarding to the fleet cost and the traveled distance. An experimental study on benchmark instances is provided to show the relevance of the proposed algorithm.

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Author information

Authors and Affiliations

  1. Institut Supérieur de Gestion, University of Tunis, 41 Rue de la liberté, 2000, Le Bardo, Tunisia

    Dhekra Rezgui

  2. ESSECT, LARODEC Laboratory, ISG, University of Tunis, Tunis, Tunisia

    Hend Bouziri

  3. Luxembourg Institute of Science and Technology, 5 Avenue des Hauts-Fourneaux, 4362, Esch-sur-Alzette, Luxembourg

    Wassila Aggoune-Mtalaa

  4. Institut des Hautes Etudes Commerciales, Carthage-2016, Carthage University, Tunis, Tunisia

    Jouhaina Chaouachi Siala

Authors
  1. Dhekra Rezgui
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  2. Hend Bouziri
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  3. Wassila Aggoune-Mtalaa
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  4. Jouhaina Chaouachi Siala
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Corresponding author

Correspondence to Dhekra Rezgui .

Editor information

Editors and Affiliations

  1. University of Macedonia, Thessaloniki, Greece

    Angelo Sifaleras

  2. University of Kent, Canterbury, UK

    Said Salhi

  3. Royal Military College of Canada, Kingston, ON, Canada

    Jack Brimberg

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Rezgui, D., Bouziri, H., Aggoune-Mtalaa, W., Siala, J.C. (2019). An Evolutionary Variable Neighborhood Descent for Addressing an Electric VRP Variant. In: Sifaleras, A., Salhi, S., Brimberg, J. (eds) Variable Neighborhood Search. ICVNS 2018. Lecture Notes in Computer Science(), vol 11328. Springer, Cham. https://doi.org/10.1007/978-3-030-15843-9_17

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  • DOI: https://doi.org/10.1007/978-3-030-15843-9_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15842-2

  • Online ISBN: 978-3-030-15843-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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