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The traveling therapist scheduling problem

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  • Jonathan Bard
  • Yufen Shao
  • Xiangtong Qi
  • Ahmad Jarrah

Abstract

This article presents a new model for constructing weekly schedules for therapists who treat patients with fixed appointment times at various healthcare facilities throughout a large geographic area. The objective is to satisfy the demand for service over a 5-day planning horizon at minimum cost subject to a variety of constraints related to time windows, overtime rules, and breaks. Each therapist works under an individually negotiated contract and may be full-time or part-time. Patient preferences for specific therapists and therapist preferences for assignments at specific facilities are also taken into account when they do not jeopardize feasibility. To gain an understanding of the computational issues, the complexity of various relaxations is examined and characterized. The results indicated that even simple versions of the problem are NP-hard. The model takes the form of a large-scale mixed-integer program but was not solvable with CPLEX for instances of realistic size. Subsequently, a branch-and-price-and-cut algorithm was developed and proved capable of finding near-optimal solutions within 50 minutes for small instances. High-quality solutions were ultimately found with a rolling horizon algorithm in a fraction of that time. The work was performed in conjunction with Key Rehab, a company that provides physical, occupational, and speech therapy services throughout the U.S. Midwest. The policies, practices, compensation rules, and legal restrictions under which Key operates are reflected in the model formulation.

Suggested Citation

  • 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.
  • Handle: RePEc:taf:uiiexx:v:46:y:2014:i:7:p:683-706
    DOI: 10.1080/0740817X.2013.851434
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    Citations

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    Cited by:

    1. Yuan Qu & Jonathan F. Bard, 2015. "A Branch-and-Price-and-Cut Algorithm for Heterogeneous Pickup and Delivery Problems with Configurable Vehicle Capacity," Transportation Science, INFORMS, vol. 49(2), pages 254-270, May.
    2. Sumin Chen & Qingcheng Zeng & Yushan Hu, 2022. "Scheduling optimization for two crossover automated stacking cranes considering relocation," Operational Research, Springer, vol. 22(3), pages 2099-2120, July.
    3. 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.
    4. Mustafa Demirbilek & Juergen Branke & Arne Strauss, 2019. "Dynamically accepting and scheduling patients for home healthcare," Health Care Management Science, Springer, vol. 22(1), pages 140-155, March.
    5. Biao Yuan & Zhibin Jiang, 2017. "Disruption Management for the Real-Time Home Caregiver Scheduling and Routing Problem," Sustainability, MDPI, vol. 9(12), pages 1-15, November.
    6. 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.
    7. Guo, Jia & Bard, Jonathan F., 2023. "A three-step optimization-based algorithm for home healthcare delivery," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    8. 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.
    9. Osman Atilla Yazır & Çağrı Koç & Eda Yücel, 2023. "The multi-period home healthcare routing and scheduling problem with electric vehicles," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(3), pages 853-901, September.

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