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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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    1. Peter Hulshof & Richard Boucherie & Erwin Hans & Johann Hurink, 2013. "Tactical resource allocation and elective patient admission planning in care processes," Health Care Management Science, Springer, vol. 16(2), pages 152-166, June.
    2. Schütz, Hans-Jörg & Kolisch, Rainer, 2012. "Approximate dynamic programming for capacity allocation in the service industry," European Journal of Operational Research, Elsevier, vol. 218(1), pages 239-250.
    3. Stephen C. Graves, 1986. "A Tactical Planning Model for a Job Shop," Operations Research, INFORMS, vol. 34(4), pages 522-533, August.
    4. Jonathan Patrick & Martin L. Puterman & Maurice Queyranne, 2008. "Dynamic Multipriority Patient Scheduling for a Diagnostic Resource," Operations Research, INFORMS, vol. 56(6), pages 1507-1525, December.
    5. Lalit Garg & Sally McClean & Brian Meenan & Peter Millard, 2010. "A non-homogeneous discrete time Markov model for admission scheduling and resource planning in a cost or capacity constrained healthcare system," Health Care Management Science, Springer, vol. 13(2), pages 155-169, June.
    6. Huseyin Topaloglu & Warren B. Powell, 2006. "Dynamic-Programming Approximations for Stochastic Time-Staged Integer Multicommodity-Flow Problems," INFORMS Journal on Computing, INFORMS, vol. 18(1), pages 31-42, February.
    7. Matthew S. Maxwell & Mateo Restrepo & Shane G. Henderson & Huseyin Topaloglu, 2010. "Approximate Dynamic Programming for Ambulance Redeployment," INFORMS Journal on Computing, INFORMS, vol. 22(2), pages 266-281, May.
    8. Schmid, Verena, 2012. "Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming," European Journal of Operational Research, Elsevier, vol. 219(3), pages 611-621.
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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. Duygu Aghazadeh & Kadir Ertogral, 2025. "A fix and optimize method based approximate dynamic programming approach for the strategic fleet sizing and delivery planning problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 33(1), pages 91-119, March.
    4. 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.
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. 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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