IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v272y2019i1d10.1007_s10479-017-2576-2.html
   My bibliography  Save this article

A skewed general variable neighborhood search algorithm with fixed threshold for the heterogeneous fleet vehicle routing problem

Author

Listed:
  • Houda Derbel

    (MODILS, FSEGS)

  • Bassem Jarboui

    (Emirates College of Technology)

  • Rim Bhiri

    (MODILS, FSEGS)

Abstract

This article considers the heterogeneous fleet vehicle routing problem, as a variant of a well-known transportation problem: the vehicle routing problem. In order to solve this particular routing problem, a variable neighborhood search with a threshold accepting mechanism is developed and implemented. The performance of the algorithm was compared to other algorithms and tested on datasets from the available literature. Computational results show that our proposed algorithm is competitive and generates new best solutions.

Suggested Citation

  • Houda Derbel & Bassem Jarboui & Rim Bhiri, 2019. "A skewed general variable neighborhood search algorithm with fixed threshold for the heterogeneous fleet vehicle routing problem," Annals of Operations Research, Springer, vol. 272(1), pages 243-272, January.
    • Handle: RePEc:spr:annopr:v:272:y:2019:i:1:d:10.1007_s10479-017-2576-2
    DOI: 10.1007/s10479-017-2576-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-017-2576-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-017-2576-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pierre Hansen & Nenad Mladenović & José Moreno Pérez, 2010. "Variable neighbourhood search: methods and applications," Annals of Operations Research, Springer, vol. 175(1), pages 367-407, March.
    2. Li, Xiangyong & Tian, Peng & Aneja, Y.P., 2010. "An adaptive memory programming metaheuristic for the heterogeneous fixed fleet vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(6), pages 1111-1127, November.
    3. Coelho, V.N. & Grasas, A. & Ramalhinho, H. & Coelho, I.M. & Souza, M.J.F. & Cruz, R.C., 2016. "An ILS-based algorithm to solve a large-scale real heterogeneous fleet VRP with multi-trips and docking constraints," European Journal of Operational Research, Elsevier, vol. 250(2), pages 367-376.
    4. Tarantilis, C.D. & Kiranoudis, C.T., 2007. "A flexible adaptive memory-based algorithm for real-life transportation operations: Two case studies from dairy and construction sector," European Journal of Operational Research, Elsevier, vol. 179(3), pages 806-822, June.
    5. Subramanian, Anand & Penna, Puca Huachi Vaz & Uchoa, Eduardo & Ochi, Luiz Satoru, 2012. "A hybrid algorithm for the Heterogeneous Fleet Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 221(2), pages 285-295.
    6. Billy E. Gillett & Leland R. Miller, 1974. "A Heuristic Algorithm for the Vehicle-Dispatch Problem," Operations Research, INFORMS, vol. 22(2), pages 340-349, April.
    7. Salhi, S. & Sari, M., 1997. "A multi-level composite heuristic for the multi-depot vehicle fleet mix problem," European Journal of Operational Research, Elsevier, vol. 103(1), pages 95-112, November.
    8. Koç, Çağrı & Bektaş, Tolga & Jabali, Ola & Laporte, Gilbert, 2016. "Thirty years of heterogeneous vehicle routing," European Journal of Operational Research, Elsevier, vol. 249(1), pages 1-21.
    9. 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.
    10. Y H Lee & J I Kim & K H Kang & K H Kim, 2008. "A heuristic for vehicle fleet mix problem using tabu search and set partitioning," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(6), pages 833-841, June.
    11. Tarantilis, C. D. & Kiranoudis, C. T. & Vassiliadis, V. S., 2004. "A threshold accepting metaheuristic for the heterogeneous fixed fleet vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 152(1), pages 148-158, January.
    12. Liu, Shuguang, 2013. "A hybrid population heuristic for the heterogeneous vehicle routing problems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 54(C), pages 67-78.
    13. Dondo, Rodolfo & Cerda, Jaime, 2007. "A cluster-based optimization approach for the multi-depot heterogeneous fleet vehicle routing problem with time windows," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1478-1507, February.
    14. Imran, Arif & Salhi, Said & Wassan, Niaz A., 2009. "A variable neighborhood-based heuristic for the heterogeneous fleet vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 197(2), pages 509-518, September.
    15. C D Tarantilis & C T Kiranoudis & V S Vassiliadis, 2003. "A list based threshold accepting metaheuristic for the heterogeneous fixed fleet vehicle routing problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(1), pages 65-71, January.
    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. Wang, Minxi & Wang, Yajie & Liu, Wei & Ma, Yu & Xiang, Longtao & Yang, Yunqi & Li, Xin, 2021. "How to achieve a win–win scenario between cost and customer satisfaction for cold chain logistics?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    2. Bochra Rabbouch & Foued Saâdaoui & Rafaa Mraihi, 2021. "Efficient implementation of the genetic algorithm to solve rich vehicle routing problems," Operational Research, Springer, vol. 21(3), pages 1763-1791, September.
    3. Subrat Sarangi & Sudipta Sarangi & Nasim S. Sabounchi, 2023. "How managerial perspectives affect the optimal fleet size and mix model: a multi-objective approach," OPSEARCH, Springer;Operational Research Society of India, vol. 60(1), pages 1-23, March.

    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. Puca Huachi Vaz Penna & Anand Subramanian & Luiz Satoru Ochi & Thibaut Vidal & Christian Prins, 2019. "A hybrid heuristic for a broad class of vehicle routing problems with heterogeneous fleet," Annals of Operations Research, Springer, vol. 273(1), pages 5-74, February.
    2. Koç, Çağrı & Bektaş, Tolga & Jabali, Ola & Laporte, Gilbert, 2016. "Thirty years of heterogeneous vehicle routing," European Journal of Operational Research, Elsevier, vol. 249(1), pages 1-21.
    3. Salhi, Said & Wassan, Niaz & Hajarat, Mutaz, 2013. "The Fleet Size and Mix Vehicle Routing Problem with Backhauls: Formulation and Set Partitioning-based Heuristics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 56(C), pages 22-35.
    4. Imran, Arif & Salhi, Said & Wassan, Niaz A., 2009. "A variable neighborhood-based heuristic for the heterogeneous fleet vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 197(2), pages 509-518, September.
    5. Subramanian, Anand & Penna, Puca Huachi Vaz & Uchoa, Eduardo & Ochi, Luiz Satoru, 2012. "A hybrid algorithm for the Heterogeneous Fleet Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 221(2), pages 285-295.
    6. Lai, David S.W. & Caliskan Demirag, Ozgun & Leung, Janny M.Y., 2016. "A tabu search heuristic for the heterogeneous vehicle routing problem on a multigraph," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 86(C), pages 32-52.
    7. Joaquín Pacheco & Rafael Caballero & Manuel Laguna & Julián Molina, 2013. "Bi-Objective Bus Routing: An Application to School Buses in Rural Areas," Transportation Science, INFORMS, vol. 47(3), pages 397-411, August.
    8. Ade Irawan, Chandra & Starita, Stefano & Chan, Hing Kai & Eskandarpour, Majid & Reihaneh, Mohammad, 2023. "Routing in offshore wind farms: A multi-period location and maintenance problem with joint use of a service operation vessel and a safe transfer boat," European Journal of Operational Research, Elsevier, vol. 307(1), pages 328-350.
    9. Liu, Shuguang, 2013. "A hybrid population heuristic for the heterogeneous vehicle routing problems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 54(C), pages 67-78.
    10. Vidal, Thibaut & Crainic, Teodor Gabriel & Gendreau, Michel & Prins, Christian, 2014. "Implicit depot assignments and rotations in vehicle routing heuristics," European Journal of Operational Research, Elsevier, vol. 237(1), pages 15-28.
    11. Tu, Wei & Fang, Zhixiang & Li, Qingquan & Shaw, Shih-Lung & Chen, BiYu, 2014. "A bi-level Voronoi diagram-based metaheuristic for a large-scale multi-depot vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 84-97.
    12. Xiaodan Wu & Ruichang Li & Chao-Hsien Chu & Richard Amoasi & Shan Liu, 2022. "Managing pharmaceuticals delivery service using a hybrid particle swarm intelligence approach," Annals of Operations Research, Springer, vol. 308(1), pages 653-684, January.
    13. 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.
    14. Oscar Dominguez & Angel A. Juan & Barry Barrios & Javier Faulin & Alba Agustin, 2016. "Using biased randomization for solving the two-dimensional loading vehicle routing problem with heterogeneous fleet," Annals of Operations Research, Springer, vol. 236(2), pages 383-404, January.
    15. Zhang, Zhenzhen & Wei, Lijun & Lim, Andrew, 2015. "An evolutionary local search for the capacitated vehicle routing problem minimizing fuel consumption under three-dimensional loading constraints," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 20-35.
    16. 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.
    17. Oscar Dominguez & Angel Juan & Barry Barrios & Javier Faulin & Alba Agustin, 2016. "Using biased randomization for solving the two-dimensional loading vehicle routing problem with heterogeneous fleet," Annals of Operations Research, Springer, vol. 236(2), pages 383-404, January.
    18. Mesut Yavuz & Ismail Çapar, 2017. "Alternative-Fuel Vehicle Adoption in Service Fleets: Impact Evaluation Through Optimization Modeling," Transportation Science, INFORMS, vol. 51(2), pages 480-493, May.
    19. Tarantilis, C.D. & Kiranoudis, C.T., 2007. "A flexible adaptive memory-based algorithm for real-life transportation operations: Two case studies from dairy and construction sector," European Journal of Operational Research, Elsevier, vol. 179(3), pages 806-822, June.
    20. Gong, Manlin & Hu, Yucong & Chen, Zhiwei & Li, Xiaopeng, 2021. "Transfer-based customized modular bus system design with passenger-route assignment optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).

    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:spr:annopr:v:272:y:2019:i:1:d:10.1007_s10479-017-2576-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.