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Staff dimensioning in homecare services with uncertain demands

Author

Listed:
  • C. Rodriguez
  • T. Garaix
  • X. Xie
  • V. Augusto

Abstract

The problem addressed in this paper is how to calculate the amount of personnel required to ensure the activity of a home health care (HHC) centre on a tactical horizon. Design of quantitative approaches for this question is challenging. The number of caregivers has to be determined for each profession in order to balance the coverage of patients in a region and the workforce cost over several months. Unknown demand in care and spatial dimensions, combination of skills to cover a care and individual trips visiting patients make the underlaying optimisation problem very hard. Few studies are dedicated to staff dimensioning for HHC compared to patient to nurses assignment/sequencing and centres location problems. We propose an original two-stage approach based on integer linear stochastic programming, which exploits historical medical data. The first stage calculates (near-)optimal levels of resources for possible demand scenarios, while the second stage computes the optimal number of caregiver for each profession to meet a target coverage indicator. For decision-makers, our algorithm gives the number of employees for each category required to satisfy the demand without any recourse (overtime, external resources) with fixed probability and confidence interval. The approach has been tested on various instances built from data of the French agency of hospitalisation data (ATIH).

Suggested Citation

  • C. Rodriguez & T. Garaix & X. Xie & V. Augusto, 2015. "Staff dimensioning in homecare services with uncertain demands," International Journal of Production Research, Taylor & Francis Journals, vol. 53(24), pages 7396-7410, December.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:24:p:7396-7410
    DOI: 10.1080/00207543.2015.1081427
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    Citations

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    Cited by:

    1. Pouria Khodabandeh & Vahid Kayvanfar & Majid Rafiee & Frank Werner, 2021. "A Bi-Objective Home Health Care Routing and Scheduling Model with Considering Nurse Downgrading Costs," IJERPH, MDPI, vol. 18(3), pages 1-24, January.
    2. 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.
    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. Cappanera, Paola & Scutellà, Maria Grazia & Nervi, Federico & Galli, Laura, 2018. "Demand uncertainty in robust Home Care optimization," Omega, Elsevier, vol. 80(C), pages 95-110.
    5. Zheng, Chenyang & Wang, Shuming & Li, Ningxin & Wu, Yuanhao, 2021. "Stochastic joint homecare service and capacity planning with nested decomposition approaches," European Journal of Operational Research, Elsevier, vol. 295(1), pages 203-222.
    6. 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.
    7. Shi, Yong & Boudouh, Toufik & Grunder, Olivier, 2019. "A robust optimization for a home health care routing and scheduling problem with consideration of uncertain travel and service times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 52-95.
    8. Nikzad, Erfaneh & Bashiri, Mahdi & Abbasi, Babak, 2021. "A matheuristic algorithm for stochastic home health care planning," European Journal of Operational Research, Elsevier, vol. 288(3), pages 753-774.
    9. Restrepo, María I. & Rousseau, Louis-Martin & Vallée, Jonathan, 2020. "Home healthcare integrated staffing and scheduling," Omega, Elsevier, vol. 95(C).
    10. 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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