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The technician routing problem with experience-based service times

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  • Chen, Xi
  • Thomas, Barrett W.
  • Hewitt, Mike

Abstract

While home services are a fast growing industry, little attention has been given to the management of its workforce. In particular, the productivity of home-service technicians depends not only on efficiently routing from customer-to-customer, but also the management of their skillsets. This paper introduces a model of technician routing that explicitly models individualized, experience-based learning. The results demonstrate that explicit modeling and the resulting ability to capture changes in productivity over time due to learning lead to significantly better and different solutions than those found when learning and workforce heterogeneity is ignored. We show that these differences result from the levels of specialization that occur in the workforce.

Suggested Citation

  • Chen, Xi & Thomas, Barrett W. & Hewitt, Mike, 2016. "The technician routing problem with experience-based service times," Omega, Elsevier, vol. 61(C), pages 49-61.
    • Handle: RePEc:eee:jomega:v:61:y:2016:i:c:p:49-61
    DOI: 10.1016/j.omega.2015.07.006
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    References listed on IDEAS

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    Cited by:

    1. Fangzhou Yan & Huaxin Qiu & Dongya Han, 2023. "Lagrangian Heuristic for Multi-Depot Technician Planning of Product Distribution and Installation with a Lunch Break," Mathematics, MDPI, vol. 11(3), pages 1-22, January.
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    3. 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.
    4. Ulmer, Marlin & Nowak, Maciek & Mattfeld, Dirk & Kaminski, Bogumił, 2020. "Binary driver-customer familiarity in service routing," European Journal of Operational Research, Elsevier, vol. 286(2), pages 477-493.
    5. Bruck, Bruno P. & Cordeau, Jean-François & Iori, Manuel, 2018. "A practical time slot management and routing problem for attended home services," Omega, Elsevier, vol. 81(C), pages 208-219.
    6. Li, Yifu & Zhou, Chenhao & Yuan, Peixue & Ngo, Thi Tu Anh, 2023. "Experience-based territory planning and driver assignment with predicted demand and driver present condition," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    7. Nowak, Maciek & Szufel, Przemysław, 2024. "Technician routing and scheduling for the sharing economy," European Journal of Operational Research, Elsevier, vol. 314(1), pages 15-31.
    8. 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.
    9. Chen, Xi & Li, Kaiwen & Lin, Sidian & Ding, Xiaosong, 2024. "Technician routing and scheduling with employees’ learning through implicit cross-training strategy," International Journal of Production Economics, Elsevier, vol. 271(C).
    10. Fleckenstein, David & Klein, Robert & Steinhardt, Claudius, 2023. "Recent advances in integrating demand management and vehicle routing: A methodological review," European Journal of Operational Research, Elsevier, vol. 306(2), pages 499-518.
    11. Vincent F. Yu & Yueh-Sheng Lin & Panca Jodiawan & Shih-Wei Lin & Yu-Chi Lai, 2023. "The Field Technician Scheduling Problem with Experience-Dependent Service Times," Mathematics, MDPI, vol. 11(21), pages 1-17, November.
    12. Chen, Xi & Hewitt, Mike & Thomas, Barrett W., 2018. "An approximate dynamic programming method for the multi-period technician scheduling problem with experience-based service times and stochastic customers," International Journal of Production Economics, Elsevier, vol. 196(C), pages 122-134.
    13. 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.
    14. Nielsen, Clara Chini & Pisinger, David, 2023. "Tactical planning for dynamic technician routing and scheduling problems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    15. Bakker, Steffen J. & Wang, Akang & Gounaris, Chrysanthos E., 2021. "Vehicle routing with endogenous learning: Application to offshore plug and abandonment campaign planning," European Journal of Operational Research, Elsevier, vol. 289(1), pages 93-106.
    16. Zhang, Jian & Woensel, Tom Van, 2023. "Dynamic vehicle routing with random requests: A literature review," International Journal of Production Economics, Elsevier, vol. 256(C).
    17. Marlin W. Ulmer & Leonard Heilig & Stefan Voß, 2017. "On the Value and Challenge of Real-Time Information in Dynamic Dispatching of Service Vehicles," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 59(3), pages 161-171, June.
    18. Albert H. Schrotenboer & Evrim Ursavas & Iris F. A. Vis, 2019. "A Branch-and-Price-and-Cut Algorithm for Resource-Constrained Pickup and Delivery Problems," Transportation Science, INFORMS, vol. 53(4), pages 1001-1022, July.
    19. John E. Fontecha & Oscar O. Guaje & Daniel Duque & Raha Akhavan-Tabatabaei & Juan P. Rodríguez & Andrés L. Medaglia, 2020. "Combined maintenance and routing optimization for large-scale sewage cleaning," Annals of Operations Research, Springer, vol. 286(1), pages 441-474, March.

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