IDEAS home Printed from https://ideas.repec.org/a/spr/joheur/v25y2019i4d10.1007_s10732-018-9385-x.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10732-018-9385-x
    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/s10732-018-9385-x?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. 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.
    2. 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.
    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.
    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. Neda Tanoumand & Tonguç Ünlüyurt, 2021. "An exact algorithm for the resource constrained home health care vehicle routing problem," Annals of Operations Research, Springer, vol. 304(1), pages 397-425, September.
    2. Pahlevani, Delaram & Abbasi, Babak & Hearne, John W. & Eberhard, Andrew, 2022. "A cluster-based algorithm for home health care planning: A case study in Australia," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    3. Paraskevopoulos, Dimitris C. & Laporte, Gilbert & Repoussis, Panagiotis P. & Tarantilis, Christos D., 2017. "Resource constrained routing and scheduling: Review and research prospects," European Journal of Operational Research, Elsevier, vol. 263(3), pages 737-754.
    4. Makboul, Salma & Kharraja, Said & Abbassi, Abderrahman & El Hilali Alaoui, Ahmed, 2024. "A multiobjective approach for weekly Green Home Health Care routing and scheduling problem with care continuity and synchronized services," Operations Research Perspectives, Elsevier, vol. 12(C).
    5. Nasir, Jamal Abdul & Kuo, Yong-Hong, 2024. "Stochastic home care transportation with dynamically prioritized patients: An integrated facility location, fleet sizing, and routing approach," Transportation Research Part B: Methodological, Elsevier, vol. 184(C).
    6. de Aguiar, Ana Raquel Pena & Ramos, Tânia Rodrigues Pereira & Gomes, Maria Isabel, 2023. "Home care routing and scheduling problem with teams’ synchronization," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    7. Castillo, Cristian & Alvarez-Palau, Eduard J. & Calvet, Laura & Panadero, Javier & Viu-Roig, Marta & Serena-Latre, Anna & Juan, Angel A., 2024. "Home healthcare in Spanish rural areas: Applying vehicle routing algorithms to health transport management," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    8. Zhan, Yang & Wang, Zizhuo & Wan, Guohua, 2021. "Home service routing and appointment scheduling with stochastic service times," European Journal of Operational Research, Elsevier, vol. 288(1), pages 98-110.
    9. Braekers, Kris & Hartl, Richard F. & Parragh, Sophie N. & Tricoire, Fabien, 2016. "A bi-objective home care scheduling problem: Analyzing the trade-off between costs and client inconvenience," European Journal of Operational Research, Elsevier, vol. 248(2), pages 428-443.
    10. Mohammed Bazirha & Abdeslam Kadrani & Rachid Benmansour, 2023. "Stochastic home health care routing and scheduling problem with multiple synchronized services," Annals of Operations Research, Springer, vol. 320(2), pages 573-601, January.
    11. Amir M. Fathollahi-Fard & Abbas Ahmadi & Behrooz Karimi, 2021. "Multi-Objective Optimization of Home Healthcare with Working-Time Balancing and Care Continuity," Sustainability, MDPI, vol. 13(22), pages 1-33, November.
    12. Andre A. Cire & Adam Diamant, 2022. "Dynamic scheduling of home care patients to medical providers," Production and Operations Management, Production and Operations Management Society, vol. 31(11), pages 4038-4056, November.
    13. Filipe Alves & Lino A. Costa & Ana Maria A. C. Rocha & Ana I. Pereira & Paulo Leitão, 2022. "The Sustainable Home Health Care Process Based on Multi-Criteria Decision-Support," Mathematics, MDPI, vol. 11(1), pages 1-19, December.
    14. Jamal Abdul Nasir & Chuangyin Dang, 2018. "Solving a More Flexible Home Health Care Scheduling and Routing Problem with Joint Patient and Nursing Staff Selection," Sustainability, MDPI, vol. 10(1), pages 1-22, January.
    15. Ehsan Pourjavad & Eman Almehdawe, 2022. "Optimization of the technician routing and scheduling problem for a telecommunication industry," Annals of Operations Research, Springer, vol. 315(1), pages 371-395, August.
    16. Christian Fikar & Angel A. Juan & Enoc Martinez & Patrick Hirsch, 2016. "A discrete-event driven metaheuristic for dynamic home service routing with synchronised trip sharing," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 10(3), pages 323-340.
    17. Michael Drexl, 2014. "A Generic Heuristic for Vehicle Routing Problems with Multiple Synchronization Constraints," Working Papers 1412, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz, revised 04 Nov 2014.
    18. Gomes, Maria Isabel & Ramos, Tânia Rodrigues Pereira, 2019. "Modelling and (re-)planning periodic home social care services with loyalty and non-loyalty features," European Journal of Operational Research, Elsevier, vol. 277(1), pages 284-299.
    19. René Bekker & Dennis Moeke & Bas Schmidt, 2019. "Keeping pace with the ebbs and flows in daily nursing home operations," Health Care Management Science, Springer, vol. 22(2), pages 350-363, June.
    20. Semih Yalçındağ & Andrea Matta & Evren Şahin & J. George Shanthikumar, 2016. "The patient assignment problem in home health care: using a data-driven method to estimate the travel times of care givers," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 304-335, June.

    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:joheur:v:25:y:2019:i:4:d:10.1007_s10732-018-9385-x. 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.