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Patient admission planning using Approximate Dynamic Programming

Author

Listed:
  • Peter J. H. Hulshof

    (University of Twente)

  • Martijn R. K. Mes

    (University of Twente)

  • Richard J. Boucherie

    (University of Twente)

  • Erwin W. Hans

    (University of Twente)

Abstract

Tactical planning in hospitals involves elective patient admission planning and the allocation of hospital resource capacities. We propose a method to develop a tactical resource allocation and patient admission plan that takes stochastic elements into consideration, thereby providing robust plans. Our method is developed in an Approximate Dynamic Programming (ADP) framework and copes with multiple resources, multiple time periods and multiple patient groups with uncertain treatment paths and an uncertain number of arrivals in each time period. As such, the method enables integrated decision making for a network of hospital departments and resources. Computational results indicate that the ADP approach provides an accurate approximation of the value functions, and that it is suitable for large problem instances at hospitals, in which the ADP approach performs significantly better than two other heuristic approaches. Our ADP algorithm is generic, as various cost functions and basis functions can be used in various hospital settings.

Suggested Citation

  • Peter J. H. Hulshof & Martijn R. K. Mes & Richard J. Boucherie & Erwin W. Hans, 2016. "Patient admission planning using Approximate Dynamic Programming," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 30-61, June.
  • Handle: RePEc:spr:flsman:v:28:y:2016:i:1:d:10.1007_s10696-015-9219-1
    DOI: 10.1007/s10696-015-9219-1
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    References listed on IDEAS

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    Cited by:

    1. Ana Batista & Jorge Vera & David Pozo, 2020. "Multi-objective admission planning problem: a two-stage stochastic approach," Health Care Management Science, Springer, vol. 23(1), pages 51-65, March.
    2. Fabian Schäfer & Manuel Walther & Alexander Hübner & Heinrich Kuhn, 2019. "Operational patient-bed assignment problem in large hospital settings including overflow and uncertainty management," Flexible Services and Manufacturing Journal, Springer, vol. 31(4), pages 1012-1041, December.
    3. Fabian Schäfer & Manuel Walther & Dominik G. Grimm & Alexander Hübner, 2023. "Combining machine learning and optimization for the operational patient-bed assignment problem," Health Care Management Science, Springer, vol. 26(4), pages 785-806, December.
    4. Hessam Bavafa & Charles M. Leys & Lerzan Örmeci & Sergei Savin, 2019. "Managing Portfolio of Elective Surgical Procedures: A Multidimensional Inverse Newsvendor Problem," Operations Research, INFORMS, vol. 67(6), pages 1543-1563, November.
    5. van Dijk, N.M. & van der Sluis, E. & Bulder, L.N. & Cui, Y., 2024. "Flexible serial capacity allocation with intensive care application," International Journal of Production Economics, Elsevier, vol. 272(C).
    6. Gökalp, E. & Gülpınar, N. & Doan, X.V., 2023. "Dynamic surgery management under uncertainty," European Journal of Operational Research, Elsevier, vol. 309(2), pages 832-844.
    7. Mojtaba Heydar & Małgorzata M. O’Reilly & Erin Trainer & Mark Fackrell & Peter G. Taylor & Ali Tirdad, 2022. "A stochastic model for the patient-bed assignment problem with random arrivals and departures," Annals of Operations Research, Springer, vol. 315(2), pages 813-845, August.
    8. Heydar, Mojtaba & Mardaneh, Elham & Loxton, Ryan, 2022. "Approximate dynamic programming for an energy-efficient parallel machine scheduling problem," European Journal of Operational Research, Elsevier, vol. 302(1), pages 363-380.

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