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Duty and workstation rostering considering preferences and fairness: a case study at a department of anaesthesiology

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  • Andreas Fügener
  • Jens O. Brunner
  • Armin Podtschaske

Abstract

This research addresses a personnel scheduling problem at hospitals. We present two mixed integer linear programming models -- a duty-roster model and a workstation-roster model. The duty-roster model determines the assignment of physicians to 24-h and late duties whereas the workstation-roster model assigns physicians to actual workstations as operating rooms. The former serves as an input for the latter. In both models, we maximise the number of assignments subject to labour regulations and internal department-specific scheduling rules. Furthermore, we consider experience levels and qualifications in our models. To promote for job satisfaction, we take into account fairness aspects as well as individual physician preferences. Using real-world data from our cooperating department of anaesthesiology with 120 physicians, we set up a case study. Computational results indicate the superior quality compared to manual scheduling which is currently in use at our cooperation hospital. Furthermore, we develop an extensive computational study with 5800 instances to test the models. We show that the computational burden is negligible and we derive managerial insight for the scheduling process.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:24:p:7465-7487
    DOI: 10.1080/00207543.2015.1082667
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    Cited by:

    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. 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.
    3. Wolbeck, Lena Antonia, 2019. "Fairness aspects in personnel scheduling," Discussion Papers 2019/16, Free University Berlin, School of Business & Economics.
    4. 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).
    5. 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.
    6. Fügener, Andreas & Pahr, Alexander & Brunner, Jens O., 2018. "Mid-term nurse rostering considering cross-training effects," International Journal of Production Economics, Elsevier, vol. 196(C), pages 176-187.
    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.

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