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Real-time scheduling optimization considering the unexpected events in home health care

Author

Listed:
  • Gang Du

    (East China Normal University)

  • Luyao Zheng

    (East China Normal University)

  • Xiaoling Ouyang

    (East China Normal University)

Abstract

Home health care, a new kind of health services that can be given in home for special populations (elderly, disabled, youth, etc.), is usually less expensive, more convenient and more efficient. Based on the face-to-face (in-person) interviews, we find that scheduling arrangements of home health care are often affected by some unexpected events such as cancellation of services, demand for emergency care and medical device failures. These events may lead to medical scheduling conflicts and therefore might decrease patient satisfaction due to the delayed service. Considering the emergencies in the home health care, this study takes the home health care mode under the unexpected events as the research object and focuses on the time window constraints involved in the real-time scheduling problem. In order to obtain an optimal medical dispatch program and ensure patients’ golden period for treatment, we establish an effective real-time scheduling model to minimize the total required time of scheduling, and propose an improved memetic algorithm to optimize the model. Empirical analysis was then adopted to verify the rationality of the model. Finally, we analyze the practical effect of dispatching decision and put forward the recommendations for future research directions.

Suggested Citation

  • Gang Du & Luyao Zheng & Xiaoling Ouyang, 2019. "Real-time scheduling optimization considering the unexpected events in home health care," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 196-220, January.
  • Handle: RePEc:spr:jcomop:v:37:y:2019:i:1:d:10.1007_s10878-017-0220-3
    DOI: 10.1007/s10878-017-0220-3
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    Cited by:

    1. Shi Yin & Jian Chang & Hailan Pan & Haizhou Mao & Mei Wang, 2021. "Early warning of venous thromboembolism after surgery based on self-organizing competitive network," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 909-927, November.
    2. Jalel Euchi & Malek Masmoudi & Patrick Siarry, 2022. "Home health care routing and scheduling problems: a literature review," 4OR, Springer, vol. 20(3), pages 351-389, September.
    3. Paola Cappanera & Maria Grazia Scutellà, 2022. "Addressing consistency and demand uncertainty in the Home Care planning problem," Flexible Services and Manufacturing Journal, Springer, vol. 34(1), pages 1-39, March.
    4. Jing Fan & Hui Shi, 0. "A three-stage supply chain scheduling problem based on the nursing assistants’ daily work in a hospital," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-13.
    5. Gang Du & Xi Liang & Xiaoling Ouyang & Chunming Wang, 2021. "Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 966-987, November.
    6. Jing Fan & Hui Shi, 2021. "A three-stage supply chain scheduling problem based on the nursing assistants’ daily work in a hospital," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 896-908, November.
    7. Shi Yin & Jian Chang & Hailan Pan & Haizhou Mao & Mei Wang, 0. "Early warning of venous thromboembolism after surgery based on self-organizing competitive network," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-19.
    8. Gang Du & Xi Liang & Xiaoling Ouyang & Chunming Wang, 0. "Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-22.

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