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The patient assignment problem in home health care: using a data-driven method to estimate the travel times of care givers

Author

Listed:
  • Semih Yalçındağ

    (Politecnico di Milano
    HEC Montréal)

  • Andrea Matta

    (Shanghai Jiao Tong University)

  • Evren Şahin

    (CentraleSupelc)

  • J. George Shanthikumar

    (Purdue University)

Abstract

Home health care is one of the recent service systems where human resource planning has a great importance. The assignment of patients to care givers is a relevant issue that the home health care service provider must address before generating the daily routes. The assignment decision is typically made without knowing the visiting sequence, which creates some uncertainties and disparities regarding the effective workload of care givers. However, taking into account travel times in the care giver workload while solving the assignment problem is not straightforward, because travel times can also be affected by clinical conditions of patients and their homes. Providing good travel time estimates that would be used in the assignment decision is the specific topic this paper focuses on. In particular, we propose a data-driven method to estimate the travel times of care givers in the assignment problem when their routes are not available yet. The method, based on the Kernel regression technique, uses the travel times observed from previous periods to estimate the time necessary for visiting a set of patients located in specific geographical locations. The main advantage offered by this technique is the empirical modelling of the travel routes generated by care givers. Numerical results based on realistic problem instances indicate that the proposed estimation method performs better than the average value and k-nearest neighbor search methods and can be successfully used in a two-stage approach that first assigns patients to care givers and then defines their routes.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:flsman:v:28:y:2016:i:1:d:10.1007_s10696-015-9222-6
    DOI: 10.1007/s10696-015-9222-6
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    References listed on IDEAS

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    Cited by:

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    2. Menteş, Nurettin & Çakmak, Mehmet Aziz & Kurt, Mehmet Emin, 2023. "Estimation of service length with the machine learning algorithms and neural networks for patients who receiving home health care," Evaluation and Program Planning, Elsevier, vol. 100(C).
    3. Gang Du & Xi Liang & Chuanwang Sun, 2017. "Scheduling Optimization of Home Health Care Service Considering Patients’ Priorities and Time Windows," Sustainability, MDPI, vol. 9(2), pages 1-22, February.
    4. Mohamed Cissé & Semih Yalçindag & Yannick Kergosien & Evren Sahin & Christophe Lenté & Andrea Matta, 2017. "OR problems related to Home Health Care: A review of relevant routing and scheduling problems," Post-Print hal-01736714, HAL.
    5. Li, Yanfeng & Xiang, Ting & Szeto, Wai Yuen, 2021. "Home health care routing and scheduling problem with the consideration of outpatient services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    6. 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.
    7. Jamal Abdul Nasir & Chuangyin Dang, 2020. "Quantitative thresholds based decision support approach for the home health care scheduling and routing problem," Health Care Management Science, Springer, vol. 23(2), pages 215-238, June.
    8. Christ, Quentin & Dauzère-Pérès, Stéphane & Lepelletier, Guillaume, 2019. "An Iterated Min–Max procedure for practical workload balancing on non-identical parallel machines in manufacturing systems," European Journal of Operational Research, Elsevier, vol. 279(2), pages 419-428.

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