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Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem

Author

Listed:
  • Haneen Algethami

    (Taif University)

  • Anna Martínez-Gavara

    (Universidad de Valencia)

  • Dario Landa-Silva

    (The University of Nottingham)

Abstract

The workforce scheduling and routing problem refers to the assignment of personnel to visits, across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise the operational cost. One of the main obstacles in designing a genetic algorithm for this problem is selecting the best set of operators that enable better GA performance. This paper presents a novel adaptive multiple crossover genetic algorithm to tackle the combined setting of scheduling and routing problems. A mix of problem-specific and traditional crossovers are evaluated by using an online learning process to measure the operator’s effectiveness. Best operators are given high application rates and low rates are given to the worse. Application rates are dynamically adjusted according to the learning outcomes in a non-stationary environment. Experimental results show that the combined performances of all the operators were better than using only one operator used in isolation. This study provided an important opportunity to advance the understanding of the best method to use crossover operators for this highly-constrained optimisation problem effectively.

Suggested Citation

  • Haneen Algethami & Anna Martínez-Gavara & Dario Landa-Silva, 2019. "Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem," Journal of Heuristics, Springer, vol. 25(4), pages 753-792, October.
    • Handle: RePEc:spr:joheur:v:25:y:2019:i:4:d:10.1007_s10732-018-9385-x
    DOI: 10.1007/s10732-018-9385-x
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    References listed on IDEAS

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    1. Sönke Hartmann, 1998. "A competitive genetic algorithm for resource‐constrained project scheduling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 45(7), pages 733-750, October.
    2. Rasmussen, Matias Sevel & Justesen, Tor & Dohn, Anders & Larsen, Jesper, 2012. "The Home Care Crew Scheduling Problem: Preference-based visit clustering and temporal dependencies," European Journal of Operational Research, Elsevier, vol. 219(3), pages 598-610.
    3. J. Arturo Castillo-Salazar & Dario Landa-Silva & Rong Qu, 2016. "Workforce scheduling and routing problems: literature survey and computational study," Annals of Operations Research, Springer, vol. 239(1), pages 39-67, April.
    4. Dorota Mankowska & Frank Meisel & Christian Bierwirth, 2014. "The home health care routing and scheduling problem with interdependent services," Health Care Management Science, Springer, vol. 17(1), pages 15-30, March.
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