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A robust multi-objective model for healthcare resource management and location planning during pandemics

Author

Listed:
  • Levent Eriskin

    (Turkish Naval Academy)

  • Mumtaz Karatas

    (Turkish Naval Academy)

  • Yu-Jun Zheng

    (Hangzhou Normal University)

Abstract

In this study, we consider the problem of healthcare resource management and location planning problem during the early stages of a pandemic/epidemic under demand uncertainty. Our main ambition is to improve the preparedness level and response effectiveness of healthcare authorities in fighting pandemics/epidemics by implementing analytical techniques. Building on lessons from the Chinese experience in the COVID-19 outbreak, we first develop a deterministic multi-objective mixed integer linear program (MILP) which determines the location and size of new pandemic hospitals (strategic level planning), periodic regional health resource re-allocations (tactical level planning) and daily patient-hospital assignments (operational level planning). Taking the forecasted number of cases along a planning horizon as an input, the model minimizes the weighted sum of the number of rejected patients, total travel distance, and installation cost of hospitals subject to real-world constraints and organizational rules. Next, accounting for the uncertainty in the spread speed of the disease, we employ an across scenario robust (ASR) model and reformulate the robust counterpart of the deterministic MILP. The ASR attains relatively more realistic solutions by considering multiple scenarios simultaneously while ensuring a predefined threshold of relative regret for the individual scenarios. Finally, we demonstrate the performance of proposed models on the case of Wuhan, China. Taking the 51 days worth of confirmed COVID-19 case data as an input, we solve both deterministic and robust models and discuss the impact of all three level decisions to the quality and performance of healthcare services during the pandemic. Our case study results show that although it is a challenging task to make strategic level decisions based on uncertain forecasted data, an immediate action can considerably improve the response effectiveness of healthcare authorities. Another important observation is that, the installation times of pandemic hospitals have significant impact on the system performance in fighting with the shortage of beds and facilities.

Suggested Citation

  • Levent Eriskin & Mumtaz Karatas & Yu-Jun Zheng, 2024. "A robust multi-objective model for healthcare resource management and location planning during pandemics," Annals of Operations Research, Springer, vol. 335(3), pages 1471-1518, April.
    • Handle: RePEc:spr:annopr:v:335:y:2024:i:3:d:10.1007_s10479-022-04760-x
    DOI: 10.1007/s10479-022-04760-x
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    References listed on IDEAS

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    1. Savachkin, Alex & Uribe, Andrés, 2012. "Dynamic redistribution of mitigation resources during influenza pandemics," Socio-Economic Planning Sciences, Elsevier, vol. 46(1), pages 33-45.
    2. Charles Revelle & David Marks & Jon C. Liebman, 1970. "An Analysis of Private and Public Sector Location Models," Management Science, INFORMS, vol. 16(11), pages 692-707, July.
    3. Chang Wook Kang & Muhammad Imran & Muhammad Omair & Waqas Ahmed & Misbah Ullah & Biswajit Sarkar, 2019. "Stochastic-Petri Net Modeling and Optimization for Outdoor Patients in Building Sustainable Healthcare System Considering Staff Absenteeism," Mathematics, MDPI, vol. 7(6), pages 1-26, June.
    4. Paul, Jomon A. & Wang, Xinfang (Jocelyn), 2019. "Robust location-allocation network design for earthquake preparedness," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 139-155.
    5. Büyüktahtakın, İ. Esra & des-Bordes, Emmanuel & Kıbış, Eyyüb Y., 2018. "A new epidemics–logistics model: Insights into controlling the Ebola virus disease in West Africa," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1046-1063.
    6. Lihui Bai & Jiang Zhang, 2014. "An incentive-based method for hospital capacity management in a pandemic: the assignment approach," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 6(4), pages 452-473.
    7. Narula, Subhash C & Ogbu, Ugonnaya I, 1979. "An hierarchal location--allocation problem," Omega, Elsevier, vol. 7(2), pages 137-143.
    8. Azrah Anparasan & Miguel Lejeune, 2019. "Resource deployment and donation allocation for epidemic outbreaks," Annals of Operations Research, Springer, vol. 283(1), pages 9-32, December.
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