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A Stochastic Programming Approach for Appointment Scheduling Under Limited Availability of Surgery Turnover Teams

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  • Serhat Gul

    (Department of Industrial Engineering, TED University, 06420 Çankaya, Ankara, Turkey)

Abstract

The number and availability of turnover teams may significantly affect the performance of a surgery schedule. We propose a two-stage stochastic integer programming formulation for setting the patient appointment times for surgeries under limited availability of turnover teams. We assume that a surgery schedule has already been created, and study how the schedule may be refined. We consider the durations of surgical operation and turnover to be random variables. The objective is to minimize the competing criteria of expected patient waiting time and operating room idle time. We discuss an implementation of a heuristic to generate near-optimal surgery schedules. We conduct numerical experiments using data from a large hospital. We compare the heuristic with a well-known and practical procedure used in earlier studies for setting patient appointment times for surgeries. Finally, we evaluate the impact of the number of turnover teams into the surgery schedules with respect to performance criteria of interest.

Suggested Citation

  • Serhat Gul, 2018. "A Stochastic Programming Approach for Appointment Scheduling Under Limited Availability of Surgery Turnover Teams," Service Science, INFORMS, vol. 10(3), pages 277-288, September.
    • Handle: RePEc:inm:orserv:v:10:y:2018:i:3:p:277-288
    DOI: serv.2018.0214
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    References listed on IDEAS

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    1. Chrwan-Jyh Ho & Hon-Shiang Lau, 1992. "Minimizing Total Cost in Scheduling Outpatient Appointments," Management Science, INFORMS, vol. 38(12), pages 1750-1764, December.
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    5. Ahmadi-Javid, Amir & Jalali, Zahra & Klassen, Kenneth J, 2017. "Outpatient appointment systems in healthcare: A review of optimization studies," European Journal of Operational Research, Elsevier, vol. 258(1), pages 3-34.
    6. S. Ayca Erdogan & Brian Denton, 2013. "Dynamic Appointment Scheduling of a Stochastic Server with Uncertain Demand," INFORMS Journal on Computing, INFORMS, vol. 25(1), pages 116-132, February.
    7. Camilo Mancilla & Robert Storer, 2012. "A sample average approximation approach to stochastic appointment sequencing and scheduling," IISE Transactions, Taylor & Francis Journals, vol. 44(8), pages 655-670.
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    Cited by:

    1. 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.
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    3. Lisa M. Maillart & Maria E. Mayorga, 2018. "Introduction to the Special Issue on Advancing Health Services," Service Science, INFORMS, vol. 10(3), pages 1-1, September.
    4. Bozkir, Cem D.C. & Ozmemis, Cagri & Kurbanzade, Ali Kaan & Balcik, Burcu & Gunes, Evrim D. & Tuglular, Serhan, 2023. "Capacity planning for effective cohorting of hemodialysis patients during the coronavirus pandemic: A case study," European Journal of Operational Research, Elsevier, vol. 304(1), pages 276-291.
    5. Çelik, Batuhan & Gul, Serhat & Çelik, Melih, 2023. "A stochastic programming approach to surgery scheduling under parallel processing principle," Omega, Elsevier, vol. 115(C).

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