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A Scenario-Based Approach for Operating Theater Scheduling Under Uncertainty

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  • Nickolas K. Freeman

    (University of Houston, Houston, Texas 77204)

  • Sharif H. Melouk

    (University of Alabama, Tuscaloosa, Alabama 35487)

  • John Mittenthal

    (University of Alabama, Tuscaloosa, Alabama 35487)

Abstract

Elective operation scheduling significantly affects the financial health of a hospital and additional metrics important to stakeholders in an operating theater (OT) environment. In this research, we develop a novel scheduling formulation that explicitly considers the uncertainty in elective operation durations and also plans for potential randomly arriving urgent demands. Using a scenario-based modeling approach, the objective of this formulation is to maximize the expected profit associated with the OT schedule. Since the complexity of the problem is NP-hard, we develop a two-step, heuristic solution approach that allows us to solve practical-sized instances in reasonable time. Experimentation shows that incorporating uncertainty via scenarios increases profit and OT utilization when compared to deterministic scheduling methods. Moreover, explicitly considering urgent arrivals results in a significant reduction in the time that patients of this type wait to receive service, with little impact on other key metrics.

Suggested Citation

  • Nickolas K. Freeman & Sharif H. Melouk & John Mittenthal, 2016. "A Scenario-Based Approach for Operating Theater Scheduling Under Uncertainty," Manufacturing & Service Operations Management, INFORMS, vol. 18(2), pages 245-261, May.
    • Handle: RePEc:inm:ormsom:v:18:y:2016:i:2:p:245-261
    DOI: 10.1287/msom.2015.0557
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    References listed on IDEAS

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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. Tsai, Shing Chih & Yeh, Yingchieh & Kuo, Chen Yun, 2021. "Efficient optimization algorithms for surgical scheduling under uncertainty," European Journal of Operational Research, Elsevier, vol. 293(2), pages 579-593.
    3. Seokjun Youn & H. Neil Geismar & Michael Pinedo, 2022. "Planning and scheduling in healthcare for better care coordination: Current understanding, trending topics, and future opportunities," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4407-4423, December.
    4. Tinglong Dai & Sridhar Tayur, 2020. "OM Forum—Healthcare Operations Management: A Snapshot of Emerging Research," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 869-887, September.
    5. Máté Hegyháti & Krisztián Attila Bakon & Tibor Holczinger, 2023. "Optimization with uncertainties: a scheduling example," 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. 31(4), pages 1239-1263, December.
    6. Jian-Jun Wang & Zongli Dai & Ai-Chih Chang & Jim Junmin Shi, 2022. "Surgical scheduling by Fuzzy model considering inpatient beds shortage under uncertain surgery durations," Annals of Operations Research, Springer, vol. 315(1), pages 463-505, August.
    7. Wang, Lien & Demeulemeester, Erik & Vansteenkiste, Nancy & Rademakers, Frank E., 2024. "Capacity and surgery partitioning: An approach for improving surgery scheduling in the inpatient surgical department," European Journal of Operational Research, Elsevier, vol. 313(1), pages 112-128.
    8. Fang, Kan & Wang, Shijin & Pinedo, Michael L. & Chen, Lin & Chu, Feng, 2021. "A combinatorial Benders decomposition algorithm for parallel machine scheduling with working-time restrictions," European Journal of Operational Research, Elsevier, vol. 291(1), pages 128-146.
    9. Omolbanin Mashkani & Andreas T. Ernst & Dhananjay Thiruvady & Hanyu Gu, 2023. "Minimizing patients total clinical condition deterioration in operating theatre departments," Annals of Operations Research, Springer, vol. 328(1), pages 821-857, September.
    10. Yu Wang & Yu Zhang & Minglong Zhou & Jiafu Tang, 2023. "Feature‐driven robust surgery scheduling," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1921-1938, June.
    11. 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.
    12. Shing Chih Tsai & Wu Hung Lin & Chia Cheng Wu & Shao Jen Weng & Ching Fen Tang, 2022. "Decision support algorithms for optimizing surgery start times considering the performance variation," Health Care Management Science, Springer, vol. 25(2), pages 208-221, June.
    13. 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.
    14. Koppka, Lisa & Wiesche, Lara & Schacht, Matthias & Werners, Brigitte, 2018. "Optimal distribution of operating hours over operating rooms using probabilities," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1156-1171.

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