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An Adaptive Large Neighborhood Search for the Periodic Vehicle Routing Problem

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Computational Logistics (ICCL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10572))

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Abstract

In this paper, an Adaptive Large Neighborhood Search (ALNS) is proposed for the Periodic Vehicle Routing Problem (PVRP). Each customer requires service on one or more days on a pre-defined time horizon. They must be assigned to feasible visit options and Vehicle Routing Problems (VRP) need to be solved for each day. In the proposed ALNS, destroy and repair operators work on the two levels of the problem. Those heuristics are rewarded which explore the search space in the beginning of the algorithm. A concept to measure if a heuristic contributes to exploring or exploiting the search space based on the dissimilarity between solution alternatives is proposed. It is investigated whether following this strategy is beneficial in terms of performance on selected instances of the PVRP. Moreover, the impact of the chosen dissimilarity measure is studied. The results show that the proposed algorithm is a promising approach for the PVRP. It pays off to reward the exploration of the search space but it is not worthwhile to use dissimilarity measures of a higher level of detail due to the increased computational effort.

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Authors and Affiliations

  1. Faculty of Management and Economics, Ruhr University Bochum, Universitätsstraße 150, 44801, Bochum, Germany

    Sandra Zajac

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  1. Sandra Zajac
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Correspondence to Sandra Zajac .

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Editors and Affiliations

  1. University of Southampton , Southampton, United Kingdom

    Tolga BektaÅŸ

  2. University of Southampton , Southampton, United Kingdom

    Stefano Coniglio

  3. University of Southampton , Southampton, United Kingdom

    Antonio Martinez-Sykora

  4. University of Hamburg , Hamburg, Germany

    Stefan Voß

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Zajac, S. (2017). An Adaptive Large Neighborhood Search for the Periodic Vehicle Routing Problem. In: Bektaş, T., Coniglio, S., Martinez-Sykora, A., Voß, S. (eds) Computational Logistics. ICCL 2017. Lecture Notes in Computer Science(), vol 10572. Springer, Cham. https://doi.org/10.1007/978-3-319-68496-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-68496-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68495-6

  • Online ISBN: 978-3-319-68496-3

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