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Flexible master surgery scheduling: combining optimization and simulation in a rolling horizon approach

Author

Listed:
  • Mariana Oliveira

    (University of Lisbon)

  • Filippo Visintin

    (University of Florence)

  • Daniel Santos

    (University of Lisbon)

  • Inês Marques

    (University of Lisbon)

Abstract

Operating room managers are facing increasingly complex challenges, namely in complying with waiting time targets before surgery. This paper proposes a framework that combines optimization and simulation to generate dynamic master surgery schedules for a long planning horizon, in which the schedules are optimized by an integer programming model and the demand levels are modelled using the simulation model. The developed approach allows the resulting operating room plan to balance waiting lists as it assigns more time to the specialties with higher demand in terms of time needed to perform all the surgeries in the corresponding waiting lists. The analysis of the results obtained for the proposed flexible rolling horizon approach were proven robust, and were compared to static and flexible long-term approaches, the former not allowing flexibility and the latter using a deterministic update of the demand. Considering throughput, tardiness and waiting time, the flexible rolling horizon approach showed the best results, while the static one had the worst results.

Suggested Citation

  • Mariana Oliveira & Filippo Visintin & Daniel Santos & Inês Marques, 2022. "Flexible master surgery scheduling: combining optimization and simulation in a rolling horizon approach," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 824-858, December.
    • Handle: RePEc:spr:flsman:v:34:y:2022:i:4:d:10.1007_s10696-021-09422-x
    DOI: 10.1007/s10696-021-09422-x
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    References listed on IDEAS

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    5. Paola Cappanera & Filippo Visintin & Carlo Banditori & Daniele Feo, 2019. "Evaluating the long-term effects of appointment scheduling policies in a magnetic resonance imaging setting," Flexible Services and Manufacturing Journal, Springer, vol. 31(1), pages 212-254, March.
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    Cited by:

    1. 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.
    2. Vincent Augusto & Nadia Lahrichi & Ettore Lanzarone & Taesik Lee & Jie Song, 2022. "Analytics and Optimization in Healthcare Management," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 821-823, December.

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