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Cooperative versus non-cooperative parallel variable neighborhood search strategies: a case study on the capacitated vehicle routing problem

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  • Panagiotis Kalatzantonakis

    (University of Macedonia)

  • Angelo Sifaleras

    (University of Macedonia)

  • Nikolaos Samaras

    (University of Macedonia)

Abstract

The capacitated vehicle routing problem (CVRP) is a well-known NP-hard combinatorial optimization problem with numerous real-world applications in logistics. In this work, we present a literature review with recent successful parallel implementations of variable neighborhood search regarding different variants of vehicle routing problems. We conduct an experimental study for the CVRP using well-known benchmark instances, and we present and investigate three parallelization strategies that coordinate the communication of the multiple processors. We experimentally evaluate a non-cooperative and two novel cooperation models, the managed cooperative and the parameterized cooperative strategies. Our results constitute a first proof-of-concept for the benefits of this new self-adaptive parameterized cooperative approach, especially in computationally hard instances.

Suggested Citation

  • Panagiotis Kalatzantonakis & Angelo Sifaleras & Nikolaos Samaras, 2020. "Cooperative versus non-cooperative parallel variable neighborhood search strategies: a case study on the capacitated vehicle routing problem," Journal of Global Optimization, Springer, vol. 78(2), pages 327-348, October.
    • Handle: RePEc:spr:jglopt:v:78:y:2020:i:2:d:10.1007_s10898-019-00866-y
    DOI: 10.1007/s10898-019-00866-y
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    References listed on IDEAS

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    1. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    2. Pierre Hansen & Nenad Mladenović & Raca Todosijević & Saïd Hanafi, 2017. "Variable neighborhood search: basics and variants," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 5(3), pages 423-454, September.
    3. Chris Groër & Bruce Golden & Edward Wasil, 2011. "A Parallel Algorithm for the Vehicle Routing Problem," INFORMS Journal on Computing, INFORMS, vol. 23(2), pages 315-330, May.
    4. Uchoa, Eduardo & Pecin, Diego & Pessoa, Artur & Poggi, Marcus & Vidal, Thibaut & Subramanian, Anand, 2017. "New benchmark instances for the Capacitated Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 257(3), pages 845-858.
    5. G. Clarke & J. W. Wright, 1964. "Scheduling of Vehicles from a Central Depot to a Number of Delivery Points," Operations Research, INFORMS, vol. 12(4), pages 568-581, August.
    6. Konstantina Skouri & Angelo Sifaleras & Ioannis Konstantaras, 2018. "Open Problems in Green Supply Chain Modeling and Optimization with Carbon Emission Targets," Springer Optimization and Its Applications, in: Panos M. Pardalos & Athanasios Migdalas (ed.), Open Problems in Optimization and Data Analysis, pages 83-90, Springer.
    7. Roberto Baldacci & Paolo Toth & Daniele Vigo, 2010. "Exact algorithms for routing problems under vehicle capacity constraints," Annals of Operations Research, Springer, vol. 175(1), pages 213-245, March.
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    1. Angelo Sifaleras & Nenad Mladenović & Panos M. Pardalos, 2020. "Preface to the special issue “ICVNS 2018”," Journal of Global Optimization, Springer, vol. 78(2), pages 239-240, October.

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