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Stochastic master surgery scheduling

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  • Bovim, Thomas Reiten
  • Christiansen, Marielle
  • Gullhav, Anders N.
  • Range, Troels Martin
  • Hellemo, Lars

Abstract

The aim of the Master Surgery Scheduling Problem (MSSP) is to schedule the medical specialties to the different operating rooms available, such that surgeries may be performed efficiently. We consider a MSSP where elective and emergency patients can be treated in the same operating rooms. In addition to elective-dedicated operating room slots, flexible operating room slots are introduced to handle the fluctuating demand of emergency patients.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:285:y:2020:i:2:p:695-711
    DOI: 10.1016/j.ejor.2020.02.001
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    References listed on IDEAS

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    1. Angela Testi & Elena Tanfani & Giancarlo Torre, 2007. "A three-phase approach for operating theatre schedules," Health Care Management Science, Springer, vol. 10(2), pages 163-172, June.
    2. Fügener, Andreas & Hans, Erwin W. & Kolisch, Rainer & Kortbeek, Nikky & Vanberkel, Peter T., 2014. "Master surgery scheduling with consideration of multiple downstream units," European Journal of Operational Research, Elsevier, vol. 239(1), pages 227-236.
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    5. Cappanera, Paola & Visintin, Filippo & Banditori, Carlo, 2014. "Comparing resource balancing criteria in master surgical scheduling: A combined optimisation-simulation approach," International Journal of Production Economics, Elsevier, vol. 158(C), pages 179-196.
    6. Adan, Ivo & Bekkers, Jos & Dellaert, Nico & Jeunet, Jully & Vissers, Jan, 2011. "Improving operational effectiveness of tactical master plans for emergency and elective patients under stochastic demand and capacitated resources," European Journal of Operational Research, Elsevier, vol. 213(1), pages 290-308, August.
    7. 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.
    8. Freeman, Nickolas & Zhao, Ming & Melouk, Sharif, 2018. "An iterative approach for case mix planning under uncertainty," Omega, Elsevier, vol. 76(C), pages 160-173.
    9. Lamiri, Mehdi & Xie, Xiaolan & Dolgui, Alexandre & Grimaud, Frederic, 2008. "A stochastic model for operating room planning with elective and emergency demand for surgery," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1026-1037, March.
    10. Koppka, Lisa & Wiesche, Lara & Schacht, Matthias & Werners, Brigitte, 2018. "Optimal distribution of operating hours over operating rooms using probabilities," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1156-1171.
    11. Cardoen, Brecht & Demeulemeester, Erik & Beliën, Jeroen, 2010. "Operating room planning and scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 201(3), pages 921-932, March.
    12. Penn, M.L. & Potts, C.N. & Harper, P.R., 2017. "Multiple criteria mixed-integer programming for incorporating multiple factors into the development of master operating theatre timetables," European Journal of Operational Research, Elsevier, vol. 262(1), pages 194-206.
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    Citations

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    Cited by:

    1. Akbarzadeh, Babak & Maenhout, Broos, 2024. "A study on policy decisions to embed flexibility for reactive recovery in the planning and scheduling process in operating rooms," Omega, Elsevier, vol. 126(C).
    2. Sean Harris & David Claudio, 2022. "Current Trends in Operating Room Scheduling 2015 to 2020: a Literature Review," SN Operations Research Forum, Springer, vol. 3(1), pages 1-42, March.
    3. Santos, Daniel & Marques, Inês, 2022. "Designing master surgery schedules with downstream unit integration via stochastic programming," European Journal of Operational Research, Elsevier, vol. 299(3), pages 834-852.
    4. 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.
    5. Jian-Jun Wang & Zongli Dai & Wenxuan Zhang & Jim Junmin Shi, 2023. "Operating room scheduling for non-operating room anesthesia with emergency uncertainty," Annals of Operations Research, Springer, vol. 321(1), pages 565-588, February.
    6. Miao Bai & Bjorn Berg & Esra Sisikoglu Sir & Mustafa Y. Sir, 2023. "Partially partitioned templating strategies for outpatient specialty practices," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 301-318, January.
    7. Tsai, Shing Chih & Yeh, Yingchieh & Kuo, Chen Yun, 2021. "Efficient optimization algorithms for surgical scheduling under uncertainty," European Journal of Operational Research, Elsevier, vol. 293(2), pages 579-593.
    8. Alves de Queiroz, Thiago & Iori, Manuel & Kramer, Arthur & Kuo, Yong-Hong, 2023. "Dynamic scheduling of patients in emergency departments," European Journal of Operational Research, Elsevier, vol. 310(1), pages 100-116.
    9. 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.
    10. Singha, Sumanta & Arha, Himanshu & Kar, Arpan Kumar, 2023. "Healthcare analytics: A techno-functional perspective," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    11. Arne Schulz & Malte Fliedner, 2023. "Minimizing the expected waiting time of emergency jobs," Journal of Scheduling, Springer, vol. 26(2), pages 147-167, April.

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