IDEAS home Printed from https://ideas.repec.org/p/ant/wpaper/2014024.html
   My bibliography  Save this paper

A large neighbourhood metaheuristic for the risk-constrained cash-in-transit vehicle routing problem

Author

Listed:
  • TALARICO, Luca
  • SÖRENSEN, Kenneth
  • SPRINGAEL, Johan

Abstract

In this paper, we propose a new metaheuristic to solve the Risk constrained Cash-in-Transit Vehicle Routing Problem (rctvrp). The rctvrp is a variant of the well-known capacitated vehicle routing problem and models the problem of routing vehicles in the cash-in-transit sector. In the rctvrp, the risk associated with a robbery represents a critical aspect that is treated as a limiting factor instead of the vehicle capacity which is typical of capacitated vehicle routing problems. The risk of being robbed is assumed to be proportional both to the amount of cash being transported and the time/distance covered by the vehicle carrying the cash. The maximum vehicle exposure to risk limited by a certain risk threshold. A new metaheuristic, called aLNS (Ant colony heuristic with Large Neighbourhood Search), is described. The aLNS metaheuristic combines the ant colony heuristic for the travelling salesman problem and a large neighbourhood search heuristic within an iterated local search heuristic framework. A new library of rctvrp instances with known optimal solutions is proposed, and split in two sets named set O and set S respectively. The aLNS algorithm is extensively tested on small, medium and large benchmark instances and compared with all existing solution approaches for the rctvrp problem.

Suggested Citation

  • TALARICO, Luca & SÖRENSEN, Kenneth & SPRINGAEL, Johan, 2014. "A large neighbourhood metaheuristic for the risk-constrained cash-in-transit vehicle routing problem," Working Papers 2014024, University of Antwerp, Faculty of Business and Economics.
    • Handle: RePEc:ant:wpaper:2014024
    as

    Download full text from publisher

    File URL: https://repository.uantwerpen.be/docman/irua/da1cc7/363f92fe.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Olli Bräysy & Michel Gendreau, 2005. "Vehicle Routing Problem with Time Windows, Part I: Route Construction and Local Search Algorithms," Transportation Science, INFORMS, vol. 39(1), pages 104-118, February.
    2. Olli Bräysy & Michel Gendreau, 2005. "Vehicle Routing Problem with Time Windows, Part II: Metaheuristics," Transportation Science, INFORMS, vol. 39(1), pages 119-139, February.
    Full references (including those not matched with items on IDEAS)

    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. Asbach, Lasse & Dorndorf, Ulrich & Pesch, Erwin, 2009. "Analysis, modeling and solution of the concrete delivery problem," European Journal of Operational Research, Elsevier, vol. 193(3), pages 820-835, March.
    2. Chen, Qingfeng & Li, Kunpeng & Liu, Zhixue, 2014. "Model and algorithm for an unpaired pickup and delivery vehicle routing problem with split loads," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 69(C), pages 218-235.
    3. Fröhlich von Elmbach, Alexander & Scholl, Armin & Walter, Rico, 2019. "Minimizing the maximal ergonomic burden in intra-hospital patient transportation," European Journal of Operational Research, Elsevier, vol. 276(3), pages 840-854.
    4. Nicolas Rincon-Garcia & Ben J. Waterson & Tom J. Cherrett, 2018. "Requirements from vehicle routing software: perspectives from literature, developers and the freight industry," Transport Reviews, Taylor & Francis Journals, vol. 38(1), pages 117-138, January.
    5. Schmid, Verena & Doerner, Karl F. & Laporte, Gilbert, 2013. "Rich routing problems arising in supply chain management," European Journal of Operational Research, Elsevier, vol. 224(3), pages 435-448.
    6. Gerhard Hiermann & Matthias Prandtstetter & Andrea Rendl & Jakob Puchinger & Günther Raidl, 2015. "Metaheuristics for solving a multimodal home-healthcare scheduling problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 23(1), pages 89-113, March.
    7. Zhang, Ruiyou & Yun, Won Young & Moon, Il Kyeong, 2011. "Modeling and optimization of a container drayage problem with resource constraints," International Journal of Production Economics, Elsevier, vol. 133(1), pages 351-359, September.
    8. Theys, Christophe & Bräysy, Olli & Dullaert, Wout & Raa, Birger, 2010. "Using a TSP heuristic for routing order pickers in warehouses," European Journal of Operational Research, Elsevier, vol. 200(3), pages 755-763, February.
    9. Chou, Chang-Chi & Chiang, Wen-Chu & Chen, Albert Y., 2022. "Emergency medical response in mass casualty incidents considering the traffic congestions in proximity on-site and hospital delays," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    10. Hideki Hashimoto & Mutsunori Yagiura & Shinji Imahori & Toshihide Ibaraki, 2013. "Recent progress of local search in handling the time window constraints of the vehicle routing problem," Annals of Operations Research, Springer, vol. 204(1), pages 171-187, April.
    11. Chiang, Wen-Chyuan & Russell, Robert & Xu, Xiaojing & Zepeda, David, 2009. "A simulation/metaheuristic approach to newspaper production and distribution supply chain problems," International Journal of Production Economics, Elsevier, vol. 121(2), pages 752-767, October.
    12. Gmira, Maha & Gendreau, Michel & Lodi, Andrea & Potvin, Jean-Yves, 2021. "Tabu search for the time-dependent vehicle routing problem with time windows on a road network," European Journal of Operational Research, Elsevier, vol. 288(1), pages 129-140.
    13. Maaike Hoogeboom & Wout Dullaert & David Lai & Daniele Vigo, 2020. "Efficient Neighborhood Evaluations for the Vehicle Routing Problem with Multiple Time Windows," Transportation Science, INFORMS, vol. 54(2), pages 400-416, March.
    14. Ullrich, Christian A., 2013. "Integrated machine scheduling and vehicle routing with time windows," European Journal of Operational Research, Elsevier, vol. 227(1), pages 152-165.
    15. Qi, Mingyao & Lin, Wei-Hua & Li, Nan & Miao, Lixin, 2012. "A spatiotemporal partitioning approach for large-scale vehicle routing problems with time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 248-257.
    16. Zäpfel, Günther & Bögl, Michael, 2008. "Multi-period vehicle routing and crew scheduling with outsourcing options," International Journal of Production Economics, Elsevier, vol. 113(2), pages 980-996, June.
    17. Jeffrey W. Ohlmann & Michael J. Fry & Barrett W. Thomas, 2008. "Route Design for Lean Production Systems," Transportation Science, INFORMS, vol. 42(3), pages 352-370, August.
    18. Hiermann, Gerhard & Puchinger, Jakob & Ropke, Stefan & Hartl, Richard F., 2016. "The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations," European Journal of Operational Research, Elsevier, vol. 252(3), pages 995-1018.
    19. Shijin Wang & Xiaodong Wang & Xin Liu & Jianbo Yu, 2018. "A Bi-Objective Vehicle-Routing Problem with Soft Time Windows and Multiple Depots to Minimize the Total Energy Consumption and Customer Dissatisfaction," Sustainability, MDPI, vol. 10(11), pages 1-21, November.
    20. Xiaopan Chen & Yunfeng Kong & Lanxue Dang & Yane Hou & Xinyue Ye, 2015. "Exact and Metaheuristic Approaches for a Bi-Objective School Bus Scheduling Problem," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-20, July.

    More about this item

    Keywords

    Vehicle routing; Risk; Security; Cash-in-transit; Metaheuristic;
    All these keywords.

    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:ant:wpaper:2014024. 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: Joeri Nys (email available below). General contact details of provider: https://edirc.repec.org/data/ftufsbe.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.