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Scheduling elective surgeries: the tradeoff among bed capacity, waiting patients and operating room utilization using goal programming

Author

Listed:
  • Xiangyong Li

    (Tongji University)

  • N. Rafaliya

    (Rice Lake Canada ULC)

  • M. Fazle Baki

    (University of Windsor)

  • Ben A. Chaouch

    (University of Windsor)

Abstract

Scheduling of surgeries in the operating rooms under limited competing resources such as surgical and nursing staff, anesthesiologist, medical equipment, and recovery beds in surgical wards is a complicated process. A well-designed schedule should be concerned with the welfare of the entire system by allocating the available resources in an efficient and effective manner. In this paper, we develop an integer linear programming model in a manner useful for multiple goals for optimally scheduling elective surgeries based on the availability of surgeons and operating rooms over a time horizon. In particular, the model is concerned with the minimization of the following important goals: (1) the anticipated number of patients waiting for service; (2) the underutilization of operating room time; (3) the maximum expected number of patients in the recovery unit; and (4) the expected range (the difference between maximum and minimum expected number) of patients in the recovery unit. We develop two goal programming (GP) models: lexicographic GP model and weighted GP model. The lexicographic GP model schedules operating rooms when various preemptive priority levels are given to these four goals. A numerical study is conducted to illustrate the optimal master-surgery schedule obtained from the models. The numerical results demonstrate that when the available number of surgeons and operating rooms is known without error over the planning horizon, the proposed models can produce good schedules and priority levels and preference weights of four goals affect the resulting schedules. The results quantify the tradeoffs that must take place as the preemptive-weights of the four goals are changed.

Suggested Citation

  • Xiangyong Li & N. Rafaliya & M. Fazle Baki & Ben A. Chaouch, 2017. "Scheduling elective surgeries: the tradeoff among bed capacity, waiting patients and operating room utilization using goal programming," Health Care Management Science, Springer, vol. 20(1), pages 33-54, March.
  • Handle: RePEc:kap:hcarem:v:20:y:2017:i:1:d:10.1007_s10729-015-9334-2
    DOI: 10.1007/s10729-015-9334-2
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    References listed on IDEAS

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    Cited by:

    1. Rachuba, Sebastian & Imhoff, Lisa & Werners, Brigitte, 2022. "Tactical blueprints for surgical weeks – An integrated approach for operating rooms and intensive care units," European Journal of Operational Research, Elsevier, vol. 298(1), pages 243-260.
    2. Bovim, Thomas Reiten & Christiansen, Marielle & Gullhav, Anders N. & Range, Troels Martin & Hellemo, Lars, 2020. "Stochastic master surgery scheduling," European Journal of Operational Research, Elsevier, vol. 285(2), pages 695-711.
    3. Duma, Davide & Aringhieri, Roberto, 2019. "The management of non-elective patients: shared vs. dedicated policies," Omega, Elsevier, vol. 83(C), pages 199-212.
    4. Steffen Heider & Jan Schoenfelder & Thomas Koperna & Jens O. Brunner, 2022. "Balancing control and autonomy in master surgery scheduling: Benefits of ICU quotas for recovery units," Health Care Management Science, Springer, vol. 25(2), pages 311-332, June.
    5. 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.
    6. 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.
    7. 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.
    8. Wenhua Li & Xing Chai, 2019. "The medical laboratory scheduling for weighted flow-time," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 83-94, January.

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