IDEAS home Printed from https://ideas.repec.org/p/jgu/wpaper/1904.html
   My bibliography  Save this paper

Large Multiple Neighborhood Search for the Soft-Clustered Vehicle-Routing Problem

Author

Listed:
  • Timo Hintsch

    (Johannes Gutenberg University Mainz)

Abstract

The soft-clustered vehicle-routing problem (SoftCluVRP) is a variant of the classical capacitated vehiclerouting problem. Customers are partitioned into clusters and all customers of the same cluster must be served by the same vehicle. In this paper, we present a large multiple neighborhood search for the SoftCluVRP. We design and analyze multiple cluster destroy and repair operators as well as two post-optimization components, which are both based on variable neighborhood descent. The first allows inter-route exchanges of complete clusters, while the second searches for intra-route improvements by combining classical neighborhoods (2- opt, Or-Opt, double-bridge) and the Balas-Simonetti neighborhood. Computational experiments show that our algorithm clearly outperforms the only existing heuristic approach from the literature. By solving benchmark instances, we provide 130 new best solutions for 220 medium-sized instances with up to 483 customers and prove 12 of them to be optimal.

Suggested Citation

  • Timo Hintsch, 2019. "Large Multiple Neighborhood Search for the Soft-Clustered Vehicle-Routing Problem," Working Papers 1904, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
  • Handle: RePEc:jgu:wpaper:1904
    as

    Download full text from publisher

    File URL: https://download.uni-mainz.de/RePEc/pdf/Discussion_Paper_1904.pdf
    File Function: First version, 2019
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David Pisinger & Stefan Ropke, 2010. "Large Neighborhood Search," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 399-419, Springer.
    2. Stefan Ropke & David Pisinger, 2006. "An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows," Transportation Science, INFORMS, vol. 40(4), pages 455-472, November.
    3. Ropke, Stefan & Pisinger, David, 2006. "A unified heuristic for a large class of Vehicle Routing Problems with Backhauls," European Journal of Operational Research, Elsevier, vol. 171(3), pages 750-775, June.
    4. Tolga Bektaş & Güneş Erdoğan & Stefan Røpke, 2011. "Formulations and Branch-and-Cut Algorithms for the Generalized Vehicle Routing Problem," Transportation Science, INFORMS, vol. 45(3), pages 299-316, August.
    5. Irnich, Stefan, 2008. "Solution of real-world postman problems," European Journal of Operational Research, Elsevier, vol. 190(1), pages 52-67, October.
    6. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
    7. Timo Hintsch & Stefan Irnich, 2018. "Exact Solution of the Soft-Clustered Vehicle Routing Problem," Working Papers 1813, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    8. Hintsch, Timo & Irnich, Stefan, 2018. "Large multiple neighborhood search for the clustered vehicle-routing problem," European Journal of Operational Research, Elsevier, vol. 270(1), pages 118-131.
    9. E. Balas, 1999. "New classes of efficiently solvable generalized Traveling Salesman Problems," Annals of Operations Research, Springer, vol. 86(0), pages 529-558, January.
    10. Alexander Butsch & Jörg Kalcsics & Gilbert Laporte, 2014. "Districting for Arc Routing," INFORMS Journal on Computing, INFORMS, vol. 26(4), pages 809-824, November.
    11. Matteo Fischetti & Juan José Salazar González & Paolo Toth, 1997. "A Branch-and-Cut Algorithm for the Symmetric Generalized Traveling Salesman Problem," Operations Research, INFORMS, vol. 45(3), pages 378-394, June.
    12. Maria Battarra & Güneş Erdoğan & Daniele Vigo, 2014. "Exact Algorithms for the Clustered Vehicle Routing Problem," Operations Research, INFORMS, vol. 62(1), pages 58-71, February.
    13. Egon Balas & Neil Simonetti, 2001. "Linear Time Dynamic-Programming Algorithms for New Classes of Restricted TSPs: A Computational Study," INFORMS Journal on Computing, INFORMS, vol. 13(1), pages 56-75, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Katrin Heßler & Stefan Irnich, 2020. "A Branch-and-Cut Algorithm for the Soft-Clustered Vehicle-Routing Problem," Working Papers 2001, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hintsch, Timo & Irnich, Stefan, 2018. "Large multiple neighborhood search for the clustered vehicle-routing problem," European Journal of Operational Research, Elsevier, vol. 270(1), pages 118-131.
    2. Timo Hintsch & Stefan Irnich, 2017. "Large Multiple Neighborhood Search for the Clustered Vehicle-Routing Problem," Working Papers 1701, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    3. Timo Hintsch & Stefan Irnich, 2018. "Exact Solution of the Soft-Clustered Vehicle Routing Problem," Working Papers 1813, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    4. Bergmann, Felix M. & Wagner, Stephan M. & Winkenbach, Matthias, 2020. "Integrating first-mile pickup and last-mile delivery on shared vehicle routes for efficient urban e-commerce distribution," Transportation Research Part B: Methodological, Elsevier, vol. 131(C), pages 26-62.
    5. Timo Hintsch & Stefan Irnich & Lone Kiilerich, 2021. "Branch-Price-and-Cut for the Soft-Clustered Capacitated Arc-Routing Problem," Transportation Science, INFORMS, vol. 55(3), pages 687-705, May.
    6. Jeanette Schmidt & Stefan Irnich, 2020. "New Neighborhoods and an Iterated Local Search Algorithm for the Generalized Traveling Salesman Problem," Working Papers 2020, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    7. Katrin Heßler & Stefan Irnich, 2020. "A Branch-and-Cut Algorithm for the Soft-Clustered Vehicle-Routing Problem," Working Papers 2001, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    8. Timo Gschwind & Michael Drexl, 2016. "Adaptive Large Neighborhood Search with a Constant-Time Feasibility Test for the Dial-a-Ride Problem," Working Papers 1624, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    9. Ruf, Moritz & Cordeau, Jean-François, 2021. "Adaptive large neighborhood search for integrated planning in railroad classification yards," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 26-51.
    10. Le Colleter, Théo & Dumez, Dorian & Lehuédé, Fabien & Péton, Olivier, 2023. "Small and large neighborhood search for the park-and-loop routing problem with parking selection," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1233-1248.
    11. Frey, Christian M.M. & Jungwirth, Alexander & Frey, Markus & Kolisch, Rainer, 2023. "The vehicle routing problem with time windows and flexible delivery locations," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1142-1159.
    12. Vidal, Thibaut & Crainic, Teodor Gabriel & Gendreau, Michel & Prins, Christian, 2013. "Heuristics for multi-attribute vehicle routing problems: A survey and synthesis," European Journal of Operational Research, Elsevier, vol. 231(1), pages 1-21.
    13. Yuan, Yuan & Cattaruzza, Diego & Ogier, Maxime & Semet, Frédéric & Vigo, Daniele, 2021. "A column generation based heuristic for the generalized vehicle routing problem with time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    14. Konrad Steiner, 2019. "Schedule-Based Integrated Inter-City Bus Line Planning for Multiple Timetabled Services via Large Multiple Neighborhood Search," Working Papers 1902, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    15. Gharehgozli, Amir & Zaerpour, Nima, 2020. "Robot scheduling for pod retrieval in a robotic mobile fulfillment system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    16. Duan, Gang & Aghalari, Amin & Chen, Li & Marufuzzaman, Mohammad & Ma, Junfeng, 2021. "Vessel routing optimization for floating macro-marine debris collection in the ocean considering dynamic velocity and direction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    17. Vidal, Thibaut & Crainic, Teodor Gabriel & Gendreau, Michel & Prins, Christian, 2014. "A unified solution framework for multi-attribute vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 234(3), pages 658-673.
    18. de Weerdt, Mathijs & Baart, Robert & He, Lei, 2021. "Single-machine scheduling with release times, deadlines, setup times, and rejection," European Journal of Operational Research, Elsevier, vol. 291(2), pages 629-639.
    19. François, Véronique & Arda, Yasemin & Crama, Yves & Laporte, Gilbert, 2016. "Large neighborhood search for multi-trip vehicle routing," European Journal of Operational Research, Elsevier, vol. 255(2), pages 422-441.
    20. Masmoudi, Mohamed Amine & Hosny, Manar & Braekers, Kris & Dammak, Abdelaziz, 2016. "Three effective metaheuristics to solve the multi-depot multi-trip heterogeneous dial-a-ride problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 60-80.

    More about this item

    Keywords

    Vehicle Routing; Clustered Vehicle Routing; Large neighborhood search;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jgu:wpaper:1904. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Research Unit IPP (email available below). General contact details of provider: https://edirc.repec.org/data/vlmaide.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.