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A study of decision support models for online patient-to-room assignment planning

Author

Listed:
  • Wim Vancroonenburg

    (KU Leuven)

  • Patrick Causmaecker

    (KU Leuven)

  • Greet Vanden Berghe

    (KU Leuven)

Abstract

The present paper studies patient-to-room assignment planning in a dynamic context. To this end, an extension of the patient assignment (PA) problem formulation is proposed, for which two online ILP-models are developed. The first model targets the optimal assignment for newly arrived patients, whereas the second also considers future, but planned, arrivals. Both models are compared on an existing set of benchmark instances from the PA planning problem, which serves as the basic problem setting. These instances are then extended with additional parameters to study the effect of uncertainty on the patients’ length of stay, as well as the effect of the percentage of emergency patients. The results show that the second model provides better results under all conditions, while still being computationally tractable. Moreover, the results show that pro-actively transferring patients from one room to another is not necessarily beneficial.

Suggested Citation

  • Wim Vancroonenburg & Patrick Causmaecker & Greet Vanden Berghe, 2016. "A study of decision support models for online patient-to-room assignment planning," Annals of Operations Research, Springer, vol. 239(1), pages 253-271, April.
    • Handle: RePEc:spr:annopr:v:239:y:2016:i:1:d:10.1007_s10479-013-1478-1
    DOI: 10.1007/s10479-013-1478-1
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    References listed on IDEAS

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    1. Krishnamoorthy, M. & Ernst, A.T. & Baatar, D., 2012. "Algorithms for large scale Shift Minimisation Personnel Task Scheduling Problems," European Journal of Operational Research, Elsevier, vol. 219(1), pages 34-48.
    2. Guinet, Alain & Chaabane, Sondes, 2003. "Operating theatre planning," International Journal of Production Economics, Elsevier, vol. 85(1), pages 69-81, July.
    3. Belien, Jeroen & Demeulemeester, Erik, 2007. "Building cyclic master surgery schedules with leveled resulting bed occupancy," European Journal of Operational Research, Elsevier, vol. 176(2), pages 1185-1204, January.
    4. Cardoen, Brecht & Demeulemeester, Erik & Beliën, Jeroen, 2010. "Operating room planning and scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 201(3), pages 921-932, March.
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    Cited by:

    1. Guido, Rosita & Groccia, Maria Carmela & Conforti, Domenico, 2018. "An efficient matheuristic for offline patient-to-bed assignment problems," European Journal of Operational Research, Elsevier, vol. 268(2), pages 486-503.
    2. Jian-Jun Wang & Zongli Dai & Wenxuan Zhang & Jim Junmin Shi, 2023. "Operating room scheduling for non-operating room anesthesia with emergency uncertainty," Annals of Operations Research, Springer, vol. 321(1), pages 565-588, February.
    3. Liu, Haichao & Wang, Yang & Hao, Jin-Kao, 2024. "Solving the patient admission scheduling problem using constraint aggregation," European Journal of Operational Research, Elsevier, vol. 316(1), pages 85-99.
    4. 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.
    5. 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.

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