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Surgery scheduling with recovery resources

Author

Listed:
  • Maya Bam
  • Brian T. Denton
  • Mark P. Van Oyen
  • Mark E. Cowen

Abstract

Surgical services are large revenue sources that account for a large portion of hospital expenses. Thus, efficient resource allocation is crucial in this system; however, this is a challenging problem, in part due to the interaction of the different stages of the surgery delivery system and the uncertainty of surgery and recovery durations. This article focuses on single-day in-patient elective surgery scheduling considering surgeons, operating rooms (ORs), and the post-anesthesia care unit (recovery). We propose a mixed-integer programming formulation of this problem and then present a fast two-phase heuristic: phase 1 is used for determining the number of ORs to open for the day and surgeon-to-OR assignments, and phase 2 is used for surgical case sequencing. Both phases have provable worst-case performance guarantees and excellent average case performance. We evaluate schedules under uncertainty using a discrete-event simulation model based on data provided by a mid-sized hospital. We show that the fast and easy-to-implement two-phase heuristic performs extremely well, in both deterministic and stochastic settings. The new methods developed reduce the computational barriers to implementation and demonstrate that hospitals can realize substantial benefits without resorting to sophisticated optimization software implementations.

Suggested Citation

  • Maya Bam & Brian T. Denton & Mark P. Van Oyen & Mark E. Cowen, 2017. "Surgery scheduling with recovery resources," IISE Transactions, Taylor & Francis Journals, vol. 49(10), pages 942-955, October.
    • Handle: RePEc:taf:uiiexx:v:49:y:2017:i:10:p:942-955
    DOI: 10.1080/24725854.2017.1325027
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    Citations

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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. Ankit Bansal & Jean-Philippe Richard & Bjorn P. Berg & Yu-Li Huang, 2024. "A Sequential Follower Refinement Algorithm for Robust Surgery Scheduling," INFORMS Journal on Computing, INFORMS, vol. 36(3), pages 918-937, May.
    4. Michael Fairley & David Scheinker & Margaret L. Brandeau, 2019. "Improving the efficiency of the operating room environment with an optimization and machine learning model," Health Care Management Science, Springer, vol. 22(4), pages 756-767, December.
    5. Yanbo Ma & Kaiyue Liu & Zheng Li & Xiang Chen, 2022. "Robust Operating Room Scheduling Model with Violation Probability Consideration under Uncertain Surgery Duration," IJERPH, MDPI, vol. 19(20), pages 1-20, October.
    6. 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.
    7. Wang, Yu & Zhang, Yu & Tang, Jiafu, 2019. "A distributionally robust optimization approach for surgery block allocation," European Journal of Operational Research, Elsevier, vol. 273(2), pages 740-753.

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