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Chance-Constrained Surgery Planning Under Conditions of Limited and Ambiguous Data

Author

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  • Yan Deng

    (Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109)

  • Siqian Shen

    (Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109)

  • Brian Denton

    (Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109)

Abstract

Surgery planning decisions include which operating rooms (ORs) to open, allocation of surgeries to ORs, sequence, and time to start each surgery. They are often made under uncertain surgery durations with limited data that lead to unknown distributional information. Moreover, cost parameters for criteria such as overtime and surgery delays are often difficult or impossible to estimate in practice. In this paper, we formulate distributionally robust (DR) chance constraints on surgery waiting and OR overtime, which recognize practical limitations on data availability and cost parameter accuracy. We use ϕ -divergence measures to build an ambiguity set of possible distributions of random surgery durations, and derive a branch-and-cut algorithm for optimizing a mixed-integer linear programming reformulation based on finite samples of the random surgery durations. We test instances generated from real hospital-based surgery data. The results show computational efficacy of our approaches, and provide insights for DR surgery planning.

Suggested Citation

  • Yan Deng & Siqian Shen & Brian Denton, 2019. "Chance-Constrained Surgery Planning Under Conditions of Limited and Ambiguous Data," INFORMS Journal on Computing, INFORMS, vol. 31(3), pages 559-575, July.
    • Handle: RePEc:inm:orijoc:v:31:y:2019:i:3:p:559-575
    DOI: 10.1287/ijoc.2018.0835
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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. Ming Zhao & Nickolas Freeman & Kai Pan, 2023. "Robust Sourcing Under Multilevel Supply Risks: Analysis of Random Yield and Capacity," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 178-195, January.
    4. Mengshi Lu & Zuo‐Jun Max Shen, 2021. "A Review of Robust Operations Management under Model Uncertainty," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1927-1943, June.
    5. 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.
    6. Yan Deng & Huiwen Jia & Shabbir Ahmed & Jon Lee & Siqian Shen, 2021. "Scenario Grouping and Decomposition Algorithms for Chance-Constrained Programs," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 757-773, May.
    7. Xinyu Yao & Karmel S. Shehadeh & Rema Padman, 2024. "Multi-resource allocation and care sequence assignment in patient management: a stochastic programming approach," Health Care Management Science, Springer, vol. 27(3), pages 352-369, September.
    8. Lu, Mengshi & Nakao, Hideaki & Shen, Siqian & Zhao, Lin, 2021. "Non-profit resource allocation and service scheduling with cross-subsidization and uncertain resource consumptions," Omega, Elsevier, vol. 99(C).
    9. Cheng Guo & Merve Bodur & Dionne M. Aleman & David R. Urbach, 2021. "Logic-Based Benders Decomposition and Binary Decision Diagram Based Approaches for Stochastic Distributed Operating Room Scheduling," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1551-1569, October.
    10. Luyun Wang & Bo Zhou, 2023. "Optimal Planning of Electric Vehicle Fast-Charging Stations Considering Uncertain Charging Demands via Dantzig–Wolfe Decomposition," Sustainability, MDPI, vol. 15(8), pages 1-23, April.
    11. Roland Braune & Walter J. Gutjahr & Petra Vogl, 2022. "Stochastic radiotherapy appointment scheduling," 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. 30(4), pages 1239-1277, December.

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