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A heuristic for vehicle fleet mix problem using tabu search and set partitioning

Author

Listed:
  • Y H Lee

    (Yonsei University)

  • J I Kim

    (Yonsei University)

  • K H Kang

    (Yonsei University)

  • K H Kim

    (Yonsei University)

Abstract

The vehicle fleet mix problem is a special case of the vehicle routing problem where customers are served by a heterogeneous fleet of vehicles with various capacities. An efficient heuristic for determining the composition of a vehicle fleet and travelling routes was developed using tabu search and by solving set partitioning problems. Two kinds of problems have appeared in the literature, concerning fixed cost and variable cost, and these were tested for evaluation. Initial solutions were found using the modified sweeping method. Whenever a new solution in an iteration of the tabu search was obtained, optimal vehicle allocation was performed for the set of routes, which are constructed from the current solution by making a giant tour. Experiments were performed for the benchmark problems that appeared in the literature and new best-known solutions were found.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:jorsoc:v:59:y:2008:i:6:d:10.1057_palgrave.jors.2602421
    DOI: 10.1057/palgrave.jors.2602421
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    References listed on IDEAS

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    Cited by:

    1. Mehdi Nourinejad & Matthew J. Roorda, 2017. "A continuous approximation model for the fleet composition problem on the rectangular grid," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(2), pages 373-401, March.
    2. 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.
    3. 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.
    4. Jabali, Ola & Gendreau, Michel & Laporte, Gilbert, 2012. "A continuous approximation model for the fleet composition problem," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1591-1606.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.

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