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A stochastic model for operating room planning under capacity constraints

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  • Aida Jebali
  • Ali Diabat

Abstract

The present paper describes a two-stage stochastic programme for operating room planning that takes into account capacity constraints of three hospital resources: operating rooms, beds in the intensive care unit (ICU) and beds in the ward (or medium care unit). Operating room planning consists of deciding on the elective surgeries to perform over each period of the planning horizon, while considering uncertainties related to surgery duration as well as patient length of stay in the ICU and the ward. Sample average approximation is then used to solve the planning problem, aiming to minimise the sum of patient-related costs and expected resource utilisation costs. Computational experiments are conducted to evaluate the performance of the proposed solution method. The obtained results highlight the robustness of operating room plans obtained by a stochastic approach, in comparison to those generated by a deterministic approach, and the importance of considering both ICU and ward beds in operating room planning.

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  • Aida Jebali & Ali Diabat, 2015. "A stochastic model for operating room planning under capacity constraints," International Journal of Production Research, Taylor & Francis Journals, vol. 53(24), pages 7252-7270, December.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:24:p:7252-7270
    DOI: 10.1080/00207543.2015.1033500
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    References listed on IDEAS

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    Cited by:

    1. Sean Harris & David Claudio, 2022. "Current Trends in Operating Room Scheduling 2015 to 2020: a Literature Review," SN Operations Research Forum, Springer, vol. 3(1), pages 1-42, March.
    2. Aisha Tayyab & Saif Ullah & Mohammed Fazle Baki, 2023. "An Outer Approximation Method for Scheduling Elective Surgeries with Sequence Dependent Setup Times to Multiple Operating Rooms," Mathematics, MDPI, vol. 11(11), pages 1-15, May.
    3. Hossein Hashemi Doulabi & Soheyl Khalilpourazari, 2023. "Stochastic weekly operating room planning with an exponential number of scenarios," Annals of Operations Research, Springer, vol. 328(1), pages 643-664, September.
    4. Zhang, Jian & Dridi, Mahjoub & El Moudni, Abdellah, 2020. "Column-generation-based heuristic approaches to stochastic surgery scheduling with downstream capacity constraints," International Journal of Production Economics, Elsevier, vol. 229(C).
    5. Arlen Dean & Amirhossein Meisami & Henry Lam & Mark P. Van Oyen & Christopher Stromblad & Nick Kastango, 2022. "Quantile regression forests for individualized surgery scheduling," Health Care Management Science, Springer, vol. 25(4), pages 682-709, December.
    6. Zhang, Jian & Dridi, Mahjoub & El Moudni, Abdellah, 2019. "A two-level optimization model for elective surgery scheduling with downstream capacity constraints," European Journal of Operational Research, Elsevier, vol. 276(2), pages 602-613.
    7. Shuwan Zhu & Wenjuan Fan & Shanlin Yang & Jun Pei & Panos M. Pardalos, 2019. "Operating room planning and surgical case scheduling: a review of literature," Journal of Combinatorial Optimization, Springer, vol. 37(3), pages 757-805, April.
    8. Shehadeh, Karmel S. & Padman, Rema, 2021. "A distributionally robust optimization approach for stochastic elective surgery scheduling with limited intensive care unit capacity," European Journal of Operational Research, Elsevier, vol. 290(3), pages 901-913.
    9. Vandenberghe, Mathieu & De Vuyst, Stijn & Aghezzaf, El-Houssaine & Bruneel, Herwig, 2019. "Surgery sequencing to minimize the expected maximum waiting time of emergent patients," European Journal of Operational Research, Elsevier, vol. 275(3), pages 971-982.
    10. Silva, Thiago A.O. & de Souza, Mauricio C., 2020. "Surgical scheduling under uncertainty by approximate dynamic programming," Omega, Elsevier, vol. 95(C).
    11. Santos, Daniel & Marques, Inês, 2022. "Designing master surgery schedules with downstream unit integration via stochastic programming," European Journal of Operational Research, Elsevier, vol. 299(3), pages 834-852.
    12. Jose M. Molina-Pariente & Erwin W. Hans & Jose M. Framinan, 2018. "A stochastic approach for solving the operating room scheduling problem," Flexible Services and Manufacturing Journal, Springer, vol. 30(1), pages 224-251, June.

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