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Developing an adaptive policy for long-term care capacity planning

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  • Yue Zhang
  • Martin Puterman

Abstract

This paper describes a refined methodology for determining long-term care (LTC) capacity levels over a multi-year planning horizon based on a previous study. The problem is to find a capacity level in each year during the planning horizon to meet a wait time service level criterion. Instead of a static policy for capacity planning, we proposal an adaptive policy, where the capacity level required in this year depends on the achieved service level in the last year as the state of the LTC system. We aggregate service levels into a few groups for tractability. Our methodology integrates a discrete event simulation for describing the LTC system and an optimization algorithm to find required capacity levels. We illustrate this methodology through a case study. The results show that the refined methodology overcomes the problems observed in the previous study. It also improves resource utilization greatly. To execute this adaptive policy in practice requires availability of surge or temporary capacity. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Yue Zhang & Martin Puterman, 2013. "Developing an adaptive policy for long-term care capacity planning," Health Care Management Science, Springer, vol. 16(3), pages 271-279, September.
    • Handle: RePEc:kap:hcarem:v:16:y:2013:i:3:p:271-279
    DOI: 10.1007/s10729-013-9229-z
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    References listed on IDEAS

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    1. Linda V. Green & Peter J. Kolesar & João Soares, 2001. "Improving the Sipp Approach for Staffing Service Systems That Have Cyclic Demands," Operations Research, INFORMS, vol. 49(4), pages 549-564, August.
    2. Yue Zhang & Martin L. Puterman & Matthew Nelson & Derek Atkins, 2012. "A Simulation Optimization Approach to Long-Term Care Capacity Planning," Operations Research, INFORMS, vol. 60(2), pages 249-261, April.
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    Cited by:

    1. Yasar A. Ozcan & Elena Tànfani & Angela Testi, 2017. "Improving the performance of surgery-based clinical pathways: a simulation-optimization approach," Health Care Management Science, Springer, vol. 20(1), pages 1-15, March.
    2. Mohammadi Bidhandi, Hadi & Patrick, Jonathan & Noghani, Pedram & Varshoei, Peyman, 2019. "Capacity planning for a network of community health services," European Journal of Operational Research, Elsevier, vol. 275(1), pages 266-279.
    3. Meisam Nasrollahi & Jafar Razmi, 2021. "A mathematical model for designing an integrated pharmaceutical supply chain with maximum expected coverage under uncertainty," Operational Research, Springer, vol. 21(1), pages 525-552, March.
    4. Elliot Lee & Mariel Lavieri & Michael Volk & Yongcai Xu, 2015. "Applying reinforcement learning techniques to detect hepatocellular carcinoma under limited screening capacity," Health Care Management Science, Springer, vol. 18(3), pages 363-375, September.

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