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Iterated local search for workforce scheduling and routing problems

Author

Listed:
  • Fulin Xie

    (CORMSIS, University of Southampton)

  • Chris N. Potts

    (CORMSIS, University of Southampton)

  • Tolga Bektaş

    (University of Southampton)

Abstract

The integration of scheduling workers to perform tasks with the traditional vehicle routing problem gives rise to the workforce scheduling and routing problems (WSRP). In the WSRP, a number of service technicians with different skills, and tasks at different locations with pre-defined time windows and skill requirements are given. It is required to find an assignment and ordering of technicians to tasks, where each task is performed within its time window by a technician with the required skill, for which the total cost of the routing is minimized. This paper describes an iterated local search (ILS) algorithm for the WSRP. The performance of the proposed algorithm is evaluated on benchmark instances against an off-the-shelf optimizer and an existing adaptive large neighbourhood search algorithm. The proposed ILS algorithm is also applied to solve the skill vehicle routing problem, which can be viewed as a special case of the WSRP. The computational results indicate that the proposed algorithm can produce high-quality solutions in short computation times.

Suggested Citation

  • Fulin Xie & Chris N. Potts & Tolga Bektaş, 2017. "Iterated local search for workforce scheduling and routing problems," Journal of Heuristics, Springer, vol. 23(6), pages 471-500, December.
    • Handle: RePEc:spr:joheur:v:23:y:2017:i:6:d:10.1007_s10732-017-9347-8
    DOI: 10.1007/s10732-017-9347-8
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    1. Vidal, Thibaut & Laporte, Gilbert & Matl, Piotr, 2020. "A concise guide to existing and emerging vehicle routing problem variants," European Journal of Operational Research, Elsevier, vol. 286(2), pages 401-416.
    2. R. B. Damm & A. A. Chaves & J. A. Riveaux & D. P. Ronconi, 2025. "Scheduling technicians and tasks through an adaptive multi-objective biased random-key genetic algorithm," Annals of Operations Research, Springer, vol. 346(2), pages 945-980, March.
    3. Gleb Belov & Natashia L. Boland & Martin W. P. Savelsbergh & Peter J. Stuckey, 2020. "Logistics optimization for a coal supply chain," Journal of Heuristics, Springer, vol. 26(2), pages 269-300, April.
    4. Zachary Steever & Kyle Hunt & Mark Karwan & Junsong Yuan & Chase C. Murray, 2024. "A Graph-Based Approach for Relating Integer Programs," INFORMS Journal on Computing, INFORMS, vol. 36(6), pages 1715-1736, December.
    5. Zachary Steever & Chase Murray & Junsong Yuan & Mark Karwan & Marco Lübbecke, 2022. "An Image-Based Approach to Detecting Structural Similarity Among Mixed Integer Programs," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 1849-1870, July.

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