IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v320y2025i3p682-698.html
   My bibliography  Save this article

A multiobjective ϵ-constraint based approach for the robust master surgical schedule under multiple uncertainties

Author

Listed:
  • Makboul, Salma
  • Olteanu, Alexandru-Liviu
  • Sevaux, Marc

Abstract

The efficient scheduling of elective surgeries in hospitals is critical for ensuring patient satisfaction, cost-effectiveness, and overall operational efficiency. However, operating theater (OT) managers face complex and competing scheduling problems due to numerous sources of uncertainty and the impact of the proposed schedule on downstream recovery units, such as the intensive care unit (ICU). To address these challenges, this study develops a multiobjective robust planning model for the weekly Master Surgical Schedule (MSS) under multiple uncertainties. The model takes into account patient priority, assignment cost and workload balancing, while also considering the constraints of the OT, surgeon availabilities, downstream resources, and the uncertainty of surgery duration and patients’ length of stay (LOS) in the ICU. To evaluate the robust solutions, a Monte Carlo simulation is used to calculate the risk of constraint violations, and an adapted ϵ-constraint algorithm is used for the four-objective problem to compute the Pareto front and calculate the hypervolume for every degree of uncertainty. This provides a comprehensive decision tool for OT decision makers and allows for the comparison of various scenarios in terms of the number of scheduled patients, canceled patients, and the utilization rate of the OT.

Suggested Citation

  • Makboul, Salma & Olteanu, Alexandru-Liviu & Sevaux, Marc, 2025. "A multiobjective ϵ-constraint based approach for the robust master surgical schedule under multiple uncertainties," European Journal of Operational Research, Elsevier, vol. 320(3), pages 682-698.
    • Handle: RePEc:eee:ejores:v:320:y:2025:i:3:p:682-698
    DOI: 10.1016/j.ejor.2024.08.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221724006593
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2024.08.022?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Shehadeh, Karmel S. & Cohn, Amy E.M. & Epelman, Marina A., 2019. "Analysis of models for the Stochastic Outpatient Procedure Scheduling Problem," European Journal of Operational Research, Elsevier, vol. 279(3), pages 721-731.
    3. Ruiz, Rubén & Vázquez-Rodríguez, José Antonio, 2010. "The hybrid flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 205(1), pages 1-18, August.
    4. 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.
    5. Kumar, Ashwani & Costa, Alysson M. & Fackrell, Mark & Taylor, Peter G., 2018. "A sequential stochastic mixed integer programming model for tactical master surgery scheduling," European Journal of Operational Research, Elsevier, vol. 270(2), pages 734-746.
    6. Adan, Ivo & Bekkers, Jos & Dellaert, Nico & Jeunet, Jully & Vissers, Jan, 2011. "Improving operational effectiveness of tactical master plans for emergency and elective patients under stochastic demand and capacitated resources," European Journal of Operational Research, Elsevier, vol. 213(1), pages 290-308, August.
    7. Bertsimas, Dimitris & Kim, Cheol Woo, 2024. "A machine learning approach to two-stage adaptive robust optimization," European Journal of Operational Research, Elsevier, vol. 319(1), pages 16-30.
    8. Hadid, Majed & Elomri, Adel & Mekkawy, Tarek El & Jouini, Oualid & Kerbache, Laoucine & Hamad, Anas, 2022. "Operations management of outpatient chemotherapy process: An optimization-oriented comprehensive review," Operations Research Perspectives, Elsevier, vol. 9(C).
    9. Vahid Roshanaei & Curtiss Luong & Dionne M. Aleman & David R. Urbach, 2017. "Collaborative Operating Room Planning and Scheduling," INFORMS Journal on Computing, INFORMS, vol. 29(3), pages 558-580, August.
    10. Gabrel, Virginie & Murat, Cécile & Thiele, Aurélie, 2014. "Recent advances in robust optimization: An overview," European Journal of Operational Research, Elsevier, vol. 235(3), pages 471-483.
    11. 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.
    12. Belií«n, Jeroen & Demeulemeester, Erik, 2008. "A branch-and-price approach for integrating nurse and surgery scheduling," European Journal of Operational Research, Elsevier, vol. 189(3), pages 652-668, September.
    13. 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.
    14. 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.
    15. Morteza Lalmazloumian & M. Fazle Baki & Majid Ahmadi, 2023. "A two-stage stochastic optimization framework to allocate operating room capacity in publicly-funded hospitals under uncertainty," Health Care Management Science, Springer, vol. 26(2), pages 238-260, June.
    16. 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.
    17. Pereira, Daniel Filipe & Oliveira, José Fernando & Carravilla, Maria Antónia, 2022. "Merging make-to-stock/make-to-order decisions into sales and operations planning: A multi-objective approach," Omega, Elsevier, vol. 107(C).
    18. Roshanaei, Vahid & Luong, Curtiss & Aleman, Dionne M. & Urbach, David, 2017. "Propagating logic-based Benders’ decomposition approaches for distributed operating room scheduling," European Journal of Operational Research, Elsevier, vol. 257(2), pages 439-455.
    19. Angela Testi & Elena Tanfani & Giancarlo Torre, 2007. "A three-phase approach for operating theatre schedules," Health Care Management Science, Springer, vol. 10(2), pages 163-172, June.
    20. Pham, Dinh-Nguyen & Klinkert, Andreas, 2008. "Surgical case scheduling as a generalized job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1011-1025, March.
    21. 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.
    22. Brian T. Denton & Andrew J. Miller & Hari J. Balasubramanian & Todd R. Huschka, 2010. "Optimal Allocation of Surgery Blocks to Operating Rooms Under Uncertainty," Operations Research, INFORMS, vol. 58(4-part-1), pages 802-816, August.
    23. 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.
    24. 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.
    25. Mohsen Varmazyar & Raha Akhavan-Tabatabaei & Nasser Salmasi & Mohammad Modarres, 2020. "Operating room scheduling problem under uncertainty: Application of continuous phase-type distributions," IISE Transactions, Taylor & Francis Journals, vol. 52(2), pages 216-235, February.
    26. 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.
    27. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
    28. Neyshabouri, Saba & Berg, Bjorn P., 2017. "Two-stage robust optimization approach to elective surgery and downstream capacity planning," European Journal of Operational Research, Elsevier, vol. 260(1), pages 21-40.
    29. Kall, P., 1982. "Stochastic programming," European Journal of Operational Research, Elsevier, vol. 10(2), pages 125-130, June.
    30. 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.
    31. Fügener, Andreas & Hans, Erwin W. & Kolisch, Rainer & Kortbeek, Nikky & Vanberkel, Peter T., 2014. "Master surgery scheduling with consideration of multiple downstream units," European Journal of Operational Research, Elsevier, vol. 239(1), pages 227-236.
    32. Makboul, Salma & Kharraja, Said & Abbassi, Abderrahman & El Hilali Alaoui, Ahmed, 2024. "A multiobjective approach for weekly Green Home Health Care routing and scheduling problem with care continuity and synchronized services," Operations Research Perspectives, Elsevier, vol. 12(C).
    33. Poss, Michael, 2014. "Robust combinatorial optimization with variable cost uncertainty," European Journal of Operational Research, Elsevier, vol. 237(3), pages 836-845.
    34. Creemers, Stefan & Beliën, Jeroen & Lambrecht, Marc, 2012. "The optimal allocation of server time slots over different classes of patients," European Journal of Operational Research, Elsevier, vol. 219(3), pages 508-521.
    35. 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.
    36. Jebali, AIda & Hadj Alouane, Atidel B. & Ladet, Pierre, 2006. "Operating rooms scheduling," International Journal of Production Economics, Elsevier, vol. 99(1-2), pages 52-62, February.
    Full references (including those not matched with items on IDEAS)

    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. 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. 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.
    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. Silva, Thiago A.O. & de Souza, Mauricio C., 2020. "Surgical scheduling under uncertainty by approximate dynamic programming," Omega, Elsevier, vol. 95(C).
    6. 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.
    7. 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.
    8. Loïc Deklerck & Babak Akbarzadeh & Broos Maenhout, 2022. "Constructing and evaluating a master surgery schedule using a service-level approach," Operational Research, Springer, vol. 22(4), pages 3663-3711, September.
    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.
    10. Majthoub Almoghrabi, Mohammed & Sagnol, Guillaume, 2025. "Surgery scheduling in flexible operating rooms by using a convex surrogate model of second-stage costs," European Journal of Operational Research, Elsevier, vol. 321(1), pages 23-40.
    11. 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.
    12. 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.
    13. 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.
    14. van den Broek d’Obrenan, Anne & Ridder, Ad & Roubos, Dennis & Stougie, Leen, 2020. "Minimizing bed occupancy variance by scheduling patients under uncertainty," European Journal of Operational Research, Elsevier, vol. 286(1), pages 336-349.
    15. Bahman Naderi & Vahid Roshanaei & Mehmet A. Begen & Dionne M. Aleman & David R. Urbach, 2021. "Increased Surgical Capacity without Additional Resources: Generalized Operating Room Planning and Scheduling," Production and Operations Management, Production and Operations Management Society, vol. 30(8), pages 2608-2635, August.
    16. Roshanaei, Vahid & Booth, Kyle E.C. & Aleman, Dionne M. & Urbach, David R. & Beck, J. Christopher, 2020. "Branch-and-check methods for multi-level operating room planning and scheduling," International Journal of Production Economics, Elsevier, vol. 220(C).
    17. Yuan Shi & Saied Mahdian & Jose Blanchet & Peter Glynn & Andrew Y. Shin & David Scheinker, 2023. "Surgical scheduling via optimization and machine learning with long-tailed data," Health Care Management Science, Springer, vol. 26(4), pages 692-718, December.
    18. 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).
    19. Roshanaei, Vahid & Naderi, Bahman, 2021. "Solving integrated operating room planning and scheduling: Logic-based Benders decomposition versus Branch-Price-and-Cut," European Journal of Operational Research, Elsevier, vol. 293(1), pages 65-78.
    20. 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.

    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:eee:ejores:v:320:y:2025:i:3:p:682-698. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.