IDEAS home Printed from https://ideas.repec.org/a/bla/popmgt/v32y2023i11p3699-3716.html
   My bibliography  Save this article

Equitable anesthesiologist scheduling under demand uncertainty using multiobjective programming

Author

Listed:
  • Kai Sun
  • Minghe Sun
  • Deepak Agrawal
  • Ronald Dravenstott
  • Frank Rosinia
  • Arkajyoti Roy

Abstract

This work addresses the practical anesthesiologist scheduling (AS) problem motivated by the needs of an academic anesthesiology department. The AS problem requires the department to plan and deploy providers to adequately meet clinical demand and institutional protocols of various clinical units over a planning horizon of up to several weeks. A data‐driven two‐step AS framework is developed by exploiting the historical demand data of anesthesia cases. The first step is a shift design which obtains the optimal shifts considering clinical demand under uncertainty using conditional value‐at‐risk constraints, and the second step is provider assignments that generate the schedule considering optimal and equitable workload distribution and provider availability using multiobjective mixed‐integer programming models. Moreover, the AS framework incorporates the provider specialties, and clinical and lifestyle preferences and aligns with the existing scheduling practices. An ɛ‐constraint solution method is applied for multiobjective optimization, and an iterative solution method is developed to improve solution quality for workload equity in clinical applications. Computational experiments are performed to evaluate the performance of three alternative forms of the workload equity objective function, and the results show that the minimization of the sum of the absolute deviations of provider workloads best balances solution runtime and quality. In the concerned academic anesthesiology department, two clinical problems, the budget and hiring planning and the monthly scheduling, are addressed via the application of the proposed AS framework. For budget and hiring, decision‐makers can make trade‐offs based on their preference using the nondominated frontiers obtained via the ɛ‐constraint method. For monthly scheduling, the iterative solution method can accommodate preassigned shifts capturing institutional requirements while improving workload equity. The workload variance has been substantially reduced from 2.92 to 1.39 after the implementation based on the historical schedule data. The provider schedule satisfaction is improved from 3.13/5 to 3.44/5, and at least 82% of scheduling burden on department leaders is relieved. The developed AS framework is generic and can be extended to the scheduling of other types of care providers, including nurses and residents.

Suggested Citation

  • Kai Sun & Minghe Sun & Deepak Agrawal & Ronald Dravenstott & Frank Rosinia & Arkajyoti Roy, 2023. "Equitable anesthesiologist scheduling under demand uncertainty using multiobjective programming," Production and Operations Management, Production and Operations Management Society, vol. 32(11), pages 3699-3716, November.
  • Handle: RePEc:bla:popmgt:v:32:y:2023:i:11:p:3699-3716
    DOI: 10.1111/poms.14058
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/poms.14058
    Download Restriction: no

    File URL: https://libkey.io/10.1111/poms.14058?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. Sandeep Rath & Kumar Rajaram, 2022. "Staff Planning for Hospitals with Implicit Cost Estimation and Stochastic Optimization," Production and Operations Management, Production and Operations Management Society, vol. 31(3), pages 1271-1289, March.
    2. Van den Bergh, Jorne & Beliën, Jeroen & De Bruecker, Philippe & Demeulemeester, Erik & De Boeck, Liesje, 2013. "Personnel scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 226(3), pages 367-385.
    3. Zhen-Yu Chen & Minghe Sun & Xi-Xi Han, 2023. "Prediction-driven collaborative emergency medical resource allocation with deep learning and optimization," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 74(2), pages 590-603, February.
    4. Topaloglu, Seyda, 2009. "A shift scheduling model for employees with different seniority levels and an application in healthcare," European Journal of Operational Research, Elsevier, vol. 198(3), pages 943-957, November.
    5. Sandeep Rath & Kumar Rajaram & Aman Mahajan, 2017. "Integrated Anesthesiologist and Room Scheduling for Surgeries: Methodology and Application," Operations Research, INFORMS, vol. 65(6), pages 1460-1478, December.
    6. David Scheinker & Margaret L. Brandeau, 2020. "Implementing Analytics Projects in a Hospital: Successes, Failures, and Opportunities," Interfaces, INFORMS, vol. 50(3), pages 176-189, May.
    7. Erhard, Melanie & Schoenfelder, Jan & Fügener, Andreas & Brunner, Jens O., 2018. "State of the art in physician scheduling," European Journal of Operational Research, Elsevier, vol. 265(1), pages 1-18.
    8. Pooyan Kazemian & Yue Dong & Thomas Rohleder & Jonathan Helm & Mark Van Oyen, 2014. "An IP-based healthcare provider shift design approach to minimize patient handoffs," Health Care Management Science, Springer, vol. 17(1), pages 1-14, March.
    9. D. Prot & T. Lapègue & O. Bellenguez-Morineau, 2015. "A two-phase method for the shift design and personnel task scheduling problem with equity objective," International Journal of Production Research, Taylor & Francis Journals, vol. 53(24), pages 7286-7298, December.
    10. Minghe Sun & Antonie Stam & Ralph E. Steuer, 1996. "Solving Multiple Objective Programming Problems Using Feed-Forward Artificial Neural Networks: The Interactive FFANN Procedure," Management Science, INFORMS, vol. 42(6), pages 835-849, June.
    11. Andreas Fügener & Jens O. Brunner & Armin Podtschaske, 2015. "Duty and workstation rostering considering preferences and fairness: a case study at a department of anaesthesiology," International Journal of Production Research, Taylor & Francis Journals, vol. 53(24), pages 7465-7487, December.
    12. Sakine Batun & Brian T. Denton & Todd R. Huschka & Andrew J. Schaefer, 2011. "Operating Room Pooling and Parallel Surgery Processing Under Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 23(2), pages 220-237, May.
    13. David Rea & Craig Froehle & Suzanne Masterson & Brian Stettler & Gregory Fermann & Arthur Pancioli, 2021. "Unequal but Fair: Incorporating Distributive Justice in Operational Allocation Models," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2304-2320, July.
    14. Kibaek Kim & Sanjay Mehrotra, 2015. "A Two-Stage Stochastic Integer Programming Approach to Integrated Staffing and Scheduling with Application to Nurse Management," Operations Research, INFORMS, vol. 63(6), pages 1431-1451, December.
    15. Salem Al-Yakoob & Hanif Sherali, 2007. "Mixed-integer programming models for an employee scheduling problem with multiple shifts and work locations," Annals of Operations Research, Springer, vol. 155(1), pages 119-142, November.
    16. Biyu He & Franklin Dexter & Alex Macario & Stefanos Zenios, 2012. "The Timing of Staffing Decisions in Hospital Operating Rooms: Incorporating Workload Heterogeneity into the Newsvendor Problem," Manufacturing & Service Operations Management, INFORMS, vol. 14(1), pages 99-114, January.
    17. Stolletz, Raik & Brunner, Jens O., 2012. "Fair optimization of fortnightly physician schedules with flexible shifts," European Journal of Operational Research, Elsevier, vol. 219(3), pages 622-629.
    18. 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.
    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. David Rea & Craig Froehle & Suzanne Masterson & Brian Stettler & Gregory Fermann & Arthur Pancioli, 2021. "Unequal but Fair: Incorporating Distributive Justice in Operational Allocation Models," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2304-2320, July.
    2. Kraul, Sebastian & Erhard, Melanie & Brunner, Jens O., 2024. "Optimizing physician schedules with resilient break assignments," Omega, Elsevier, vol. 129(C).
    3. Damcı-Kurt, Pelin & Zhang, Minjiao & Marentay, Brian & Govind, Nirmal, 2019. "Improving physician schedules by leveraging equalization: Cases from hospitals in U.S," Omega, Elsevier, vol. 85(C), pages 182-193.
    4. Wolbeck, Lena Antonia, 2019. "Fairness aspects in personnel scheduling," Discussion Papers 2019/16, Free University Berlin, School of Business & Economics.
    5. Erhard, Melanie & Schoenfelder, Jan & Fügener, Andreas & Brunner, Jens O., 2018. "State of the art in physician scheduling," European Journal of Operational Research, Elsevier, vol. 265(1), pages 1-18.
    6. Kraul, Sebastian & Brunner, Jens O., 2023. "Stable annual scheduling of medical residents using prioritized multiple training schedules to combat operational uncertainty," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1263-1278.
    7. Sandeep Rath & Kumar Rajaram, 2022. "Staff Planning for Hospitals with Implicit Cost Estimation and Stochastic Optimization," Production and Operations Management, Production and Operations Management Society, vol. 31(3), pages 1271-1289, March.
    8. Melanie Erhard, 2021. "Flexible staffing of physicians with column generation," Flexible Services and Manufacturing Journal, Springer, vol. 33(1), pages 212-252, March.
    9. Wang, Fan & Zhang, Chao & Zhang, Hui & Xu, Liang, 2021. "Short-term physician rescheduling model with feature-driven demand for mental disorders outpatients," Omega, Elsevier, vol. 105(C).
    10. Jan Schoenfelder & Christian Pfefferlen, 2018. "Decision Support for the Physician Scheduling Process at a German Hospital," Service Science, INFORMS, vol. 10(3), pages 215-229, September.
    11. Andreas Fügener & Jens O. Brunner, 2019. "Planning for Overtime: The Value of Shift Extensions in Physician Scheduling," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 732-744, October.
    12. Paola Cappanera & Filippo Visintin & Roberta Rossi, 2022. "The emergency department physician rostering problem: obtaining equitable solutions via network optimization," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 916-959, December.
    13. Dina Bentayeb & Nadia Lahrichi & Louis-Martin Rousseau, 2023. "On integrating patient appointment grids and technologist schedules in a radiology center," Health Care Management Science, Springer, vol. 26(1), pages 62-78, March.
    14. Farzad Zaerpour & Marco Bijvank & Huiyin Ouyang & Zhankun Sun, 2022. "Scheduling of Physicians with Time‐Varying Productivity Levels in Emergency Departments," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 645-667, February.
    15. Renata Mansini & Roberto Zanotti, 2020. "Optimizing the physician scheduling problem in a large hospital ward," Journal of Scheduling, Springer, vol. 23(3), pages 337-361, June.
    16. Volland, Jonas & Fügener, Andreas & Brunner, Jens O., 2017. "A column generation approach for the integrated shift and task scheduling problem of logistics assistants in hospitals," European Journal of Operational Research, Elsevier, vol. 260(1), pages 316-334.
    17. 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.
    18. Van den Bergh, Jorne & Beliën, Jeroen & De Bruecker, Philippe & Demeulemeester, Erik & De Boeck, Liesje, 2013. "Personnel scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 226(3), pages 367-385.
    19. Douglas S. Altner & Erica K. Mason & Les D. Servi, 2019. "Two-stage stochastic days-off scheduling of multi-skilled analysts with training options," Journal of Combinatorial Optimization, Springer, vol. 38(1), pages 111-129, July.
    20. Wang, Ziwei & Chen, Hongmin & Luo, Jun & Wang, Chunming & Xu, Xinyi & Zhou, Ying, 2024. "Sharing service in healthcare systems: A recent survey," Omega, Elsevier, vol. 129(C).

    More about this item

    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:bla:popmgt:v:32:y:2023:i:11:p:3699-3716. 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: Wiley Content Delivery (email available below). General contact details of provider: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1937-5956 .

    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.