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A diving heuristic for planning and scheduling surgical cases in the operating room department with nurse re-rostering

(2020) JOURNAL OF SCHEDULING. 23(2). p.265-288
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Abstract
The decisions in the operating room scheduling process related to the case mix planning, the master surgery schedule and the nurse roster are based on the expected demand, predicted by historical data. Patients are only scheduled in the operational phase when the actual demand is known. However, the actual patient demand may differ from the expected demand. In this paper, we integrate the surgical case planning and scheduling problem and include the nurse re-rostering decision and nurse assignment to specific patients in order to utilise the operating room department as efficiently as possible and maximise the operating room profit. We propose a two-phase heuristic that uses the LP solution generated via column generation to construct a high-quality feasible solution. Computational experiments have been conducted on a diverse artificial data set generated in a controlled and structured manner and real-life data from the Sina Hospital (Tehran, Iran). We show that the presented approach is able to produce (near-)optimal solutions and benchmark the procedure with other optimisation strategies and solution methodologies.
Keywords
Management Science and Operations Research, General Engineering, Software, Artificial Intelligence, Surgical case planning and scheduling, Nurse re-rostering, Nurse patient assignment, Column generation, Diving heuristic, THEATER, BRANCH, PRICE, DEMAND

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MLA
Akbarzadeh, Babak, et al. “A Diving Heuristic for Planning and Scheduling Surgical Cases in the Operating Room Department with Nurse Re-Rostering.” JOURNAL OF SCHEDULING, vol. 23, no. 2, 2020, pp. 265–88, doi:10.1007/s10951-020-00639-6.
APA
Akbarzadeh, B., Moslehi, G., Reisi-Nafchi, M., & Maenhout, B. (2020). A diving heuristic for planning and scheduling surgical cases in the operating room department with nurse re-rostering. JOURNAL OF SCHEDULING, 23(2), 265–288. https://doi.org/10.1007/s10951-020-00639-6
Chicago author-date
Akbarzadeh, Babak, Ghasem Moslehi, Mohammad Reisi-Nafchi, and Broos Maenhout. 2020. “A Diving Heuristic for Planning and Scheduling Surgical Cases in the Operating Room Department with Nurse Re-Rostering.” JOURNAL OF SCHEDULING 23 (2): 265–88. https://doi.org/10.1007/s10951-020-00639-6.
Chicago author-date (all authors)
Akbarzadeh, Babak, Ghasem Moslehi, Mohammad Reisi-Nafchi, and Broos Maenhout. 2020. “A Diving Heuristic for Planning and Scheduling Surgical Cases in the Operating Room Department with Nurse Re-Rostering.” JOURNAL OF SCHEDULING 23 (2): 265–288. doi:10.1007/s10951-020-00639-6.
Vancouver
1.
Akbarzadeh B, Moslehi G, Reisi-Nafchi M, Maenhout B. A diving heuristic for planning and scheduling surgical cases in the operating room department with nurse re-rostering. JOURNAL OF SCHEDULING. 2020;23(2):265–88.
IEEE
[1]
B. Akbarzadeh, G. Moslehi, M. Reisi-Nafchi, and B. Maenhout, “A diving heuristic for planning and scheduling surgical cases in the operating room department with nurse re-rostering,” JOURNAL OF SCHEDULING, vol. 23, no. 2, pp. 265–288, 2020.
@article{8644953,
  abstract     = {{The decisions in the operating room scheduling process related to the case mix planning, the master surgery schedule and the nurse roster are based on the expected demand, predicted by historical data. Patients are only scheduled in the operational phase when the actual demand is known. However, the actual patient demand may differ from the expected demand. In this paper, we integrate the surgical case planning and scheduling problem and include the nurse re-rostering decision and nurse assignment to specific patients in order to utilise the operating room department as efficiently as possible and maximise the operating room profit. We propose a two-phase heuristic that uses the LP solution generated via column generation to construct a high-quality feasible solution. Computational experiments have been conducted on a diverse artificial data set generated in a controlled and structured manner and real-life data from the Sina Hospital (Tehran, Iran). We show that the presented approach is able to produce (near-)optimal solutions and benchmark the procedure with other optimisation strategies and solution methodologies.}},
  author       = {{Akbarzadeh, Babak and Moslehi, Ghasem and Reisi-Nafchi, Mohammad and Maenhout, Broos}},
  issn         = {{1094-6136}},
  journal      = {{JOURNAL OF SCHEDULING}},
  keywords     = {{Management Science and Operations Research,General Engineering,Software,Artificial Intelligence,Surgical case planning and scheduling,Nurse re-rostering,Nurse patient assignment,Column generation,Diving heuristic,THEATER,BRANCH,PRICE,DEMAND}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{265--288}},
  title        = {{A diving heuristic for planning and scheduling surgical cases in the operating room department with nurse re-rostering}},
  url          = {{http://dx.doi.org/10.1007/s10951-020-00639-6}},
  volume       = {{23}},
  year         = {{2020}},
}

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