IDEAS home Printed from https://ideas.repec.org/a/inm/ormsom/v18y2016i2p245-261.html
   My bibliography  Save this article

A Scenario-Based Approach for Operating Theater Scheduling Under Uncertainty

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/msom.2015.0557
    Download Restriction: no
    File URL: https://libkey.io/10.1287/msom.2015.0557?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. James C. Bean & John R. Birge & John Mittenthal & Charles E. Noon, 1991. "Matchup Scheduling with Multiple Resources, Release Dates and Disruptions," Operations Research, INFORMS, vol. 39(3), pages 470-483, June.
    2. Hans, Erwin & Wullink, Gerhard & van Houdenhoven, Mark & Kazemier, Geert, 2008. "Robust surgery loading," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1038-1050, March.
    3. Wang, Yu & Tang, Jiafu & Fung, Richard Y.K., 2014. "A column-generation-based heuristic algorithm for solving operating theater planning problem under stochastic demand and surgery cancellation risk," International Journal of Production Economics, Elsevier, vol. 158(C), pages 28-36.
    4. Brian Denton & James Viapiano & Andrea Vogl, 2007. "Optimization of surgery sequencing and scheduling decisions under uncertainty," Health Care Management Science, Springer, vol. 10(1), pages 13-24, February.
    5. Lamiri, Mehdi & Xie, Xiaolan & Dolgui, Alexandre & Grimaud, Frederic, 2008. "A stochastic model for operating room planning with elective and emergency demand for surgery," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1026-1037, March.
    6. Lamiri, Mehdi & Grimaud, Frédéric & Xie, Xiaolan, 2009. "Optimization methods for a stochastic surgery planning problem," International Journal of Production Economics, Elsevier, vol. 120(2), pages 400-410, August.
    7. Sebastian Rachuba & Brigitte Werners, 2014. "A robust approach for scheduling in hospitals using multiple objectives," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(4), pages 546-556, April.
    8. Min, Daiki & Yih, Yuehwern, 2010. "Scheduling elective surgery under uncertainty and downstream capacity constraints," European Journal of Operational Research, Elsevier, vol. 206(3), pages 642-652, November.
    9. Francesca Guerriero & Rosita Guido, 2011. "Operational research in the management of the operating theatre: a survey," Health Care Management Science, Springer, vol. 14(1), pages 89-114, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    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. 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.
    5. 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.
    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. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michael Samudra & Carla Van Riet & Erik Demeulemeester & Brecht Cardoen & Nancy Vansteenkiste & Frank E. Rademakers, 2016. "Scheduling operating rooms: achievements, challenges and pitfalls," Journal of Scheduling, Springer, vol. 19(5), pages 493-525, October.
    2. Silva, Thiago A.O. & de Souza, Mauricio C., 2020. "Surgical scheduling under uncertainty by approximate dynamic programming," Omega, Elsevier, vol. 95(C).
    3. Marques, Inês & Captivo, M. Eugénia, 2017. "Different stakeholders’ perspectives for a surgical case assignment problem: Deterministic and robust approaches," European Journal of Operational Research, Elsevier, vol. 261(1), pages 260-278.
    4. 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.
    5. 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.
    6. Michael Samudra & Erik Demeulemeester & Brecht Cardoen & Nancy Vansteenkiste & Frank E. Rademakers, 2017. "Due time driven surgery scheduling," Health Care Management Science, Springer, vol. 20(3), pages 326-352, September.
    7. Freeman, Nickolas & Zhao, Ming & Melouk, Sharif, 2018. "An iterative approach for case mix planning under uncertainty," Omega, Elsevier, vol. 76(C), pages 160-173.
    8. 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.
    9. Zhang, Yu & Wang, Yu & Tang, Jiafu & Lim, Andrew, 2020. "Mitigating overtime risk in tactical surgical scheduling," Omega, Elsevier, vol. 93(C).
    10. 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.
    11. 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.
    12. Francesca Guerriero & Rosita Guido, 2011. "Operational research in the management of the operating theatre: a survey," Health Care Management Science, Springer, vol. 14(1), pages 89-114, March.
    13. Sebastian Rachuba & Brigitte Werners, 2017. "A fuzzy multi-criteria approach for robust operating room schedules," Annals of Operations Research, Springer, vol. 251(1), pages 325-350, April.
    14. Bernardetta Addis & Giuliana Carello & Andrea Grosso & Elena Tànfani, 2016. "Operating room scheduling and rescheduling: a rolling horizon approach," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 206-232, June.
    15. Gartner, Daniel & Kolisch, Rainer, 2014. "Scheduling the hospital-wide flow of elective patients," European Journal of Operational Research, Elsevier, vol. 233(3), pages 689-699.
    16. 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.
    17. Cappanera, Paola & Visintin, Filippo & Banditori, Carlo, 2014. "Comparing resource balancing criteria in master surgical scheduling: A combined optimisation-simulation approach," International Journal of Production Economics, Elsevier, vol. 158(C), pages 179-196.
    18. Aringhieri, Roberto & Duma, Davide & Landa, Paolo & Mancini, Simona, 2022. "Combining workload balance and patient priority maximisation in operating room planning through hierarchical multi-objective optimisation," European Journal of Operational Research, Elsevier, vol. 298(2), pages 627-643.
    19. 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.
    20. Eun, Joonyup & Kim, Sang-Phil & Yih, Yuehwern & Tiwari, Vikram, 2019. "Scheduling elective surgery patients considering time-dependent health urgency: Modeling and solution approaches," Omega, Elsevier, vol. 86(C), pages 137-153.

    More about this item

    Keywords

    healthcare management; math programming;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormsom:v:18:y:2016:i:2:p:245-261. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.