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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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    1. Lee, Wen-Chiung & Wu, Chin-Chia & Hsu, Peng-Hsiang, 2010. "A single-machine learning effect scheduling problem with release times," Omega, Elsevier, vol. 38(1-2), pages 3-11, February.
    2. Lai, Peng-Jen & Lee, Wen-Chiung, 2011. "Single-machine scheduling with general sum-of-processing-time-based and position-based learning effects," Omega, Elsevier, vol. 39(5), pages 467-471, October.
    3. Yufen Shao & Jonathan Bard & Ahmad Jarrah, 2012. "The therapist routing and scheduling problem," IISE Transactions, Taylor & Francis Journals, vol. 44(10), pages 868-893.
    4. Cortés, Cristián E. & Gendreau, Michel & Rousseau, Louis Martin & Souyris, Sebastián & Weintraub, Andrés, 2014. "Branch-and-price and constraint programming for solving a real-life technician dispatching problem," European Journal of Operational Research, Elsevier, vol. 238(1), pages 300-312.
    5. David Lesaint & Christos Voudouris & Nader Azarmi, 2000. "Dynamic Workforce Scheduling for British Telecommunications plc," Interfaces, INFORMS, vol. 30(1), pages 45-56, February.
    6. Hideki Hashimoto & Sylvain Boussier & Michel Vasquez & Christophe Wilbaut, 2011. "A GRASP-based approach for technicians and interventions scheduling for telecommunications," Annals of Operations Research, Springer, vol. 183(1), pages 143-161, March.
    7. Biskup, Dirk, 2008. "A state-of-the-art review on scheduling with learning effects," European Journal of Operational Research, Elsevier, vol. 188(2), pages 315-329, July.
    8. Jaber, Mohamad Y. & Sikstrom, Sverker, 2004. "A numerical comparison of three potential learning and forgetting models," International Journal of Production Economics, Elsevier, vol. 92(3), pages 281-294, December.
    9. Jonathan Bard & Yufen Shao & Xiangtong Qi & Ahmad Jarrah, 2014. "The traveling therapist scheduling problem," IISE Transactions, Taylor & Francis Journals, vol. 46(7), pages 683-706.
    10. Hongsheng Zhong & Randolph W. Hall & Maged Dessouky, 2007. "Territory Planning and Vehicle Dispatching with Driver Learning," Transportation Science, INFORMS, vol. 41(1), pages 74-89, February.
    11. Janiak, Adam & Rudek, RadosLaw, 2010. "A note on a makespan minimization problem with a multi-ability learning effect," Omega, Elsevier, vol. 38(3-4), pages 213-217, June.
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    3. 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.
    4. 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.
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    8. 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).
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
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    19. 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.

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