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A simulation based approximate dynamic programming approach to multi-class, multi-resource surgical scheduling

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  • Astaraky, Davood
  • Patrick, Jonathan

Abstract

This paper presents a model and solution methodology for scheduling patients in a multi-class, multi-resource surgical system. Specifically, given a master schedule that provides a cyclic breakdown of total OR availability into specific daily allocations to each surgical specialty, the model provides a scheduling policy for all surgeries that minimizes a combination of the lead time between patient request and surgery date, overtime in the operating room and congestion in the wards. To the best of our knowledge, this paper is the first to determine a surgical schedule based on making efficient use of both the operating rooms and the recovery beds. Such a problem can be formulated as Markov Decision Process model but the size of any realistic problem makes traditional solution methods intractable. We develop a version of the Least Squares Approximate Policy Iteration algorithm and test our model on data from a local hospital to demonstrate the success of the resulting policy.

Suggested Citation

  • Astaraky, Davood & Patrick, Jonathan, 2015. "A simulation based approximate dynamic programming approach to multi-class, multi-resource surgical scheduling," European Journal of Operational Research, Elsevier, vol. 245(1), pages 309-319.
    • Handle: RePEc:eee:ejores:v:245:y:2015:i:1:p:309-319
    DOI: 10.1016/j.ejor.2015.02.032
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    References listed on IDEAS

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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.
    2. Arne Schulz, 2023. "The balanced maximally diverse grouping problem with integer attribute values," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-27, July.
    3. Agrawal, Deepak & Pang, Guodong & Kumara, Soundar, 2023. "Preference based scheduling in a healthcare provider network," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1318-1335.
    4. 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.
    5. Vusal Babashov & Antoine Sauré & Onur Ozturk & Jonathan Patrick, 2023. "Setting wait time targets in a multi‐priority patient setting," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1958-1974, June.
    6. Gang Du & Xinyue Li & Hui Hu & Xiaoling Ouyang, 2018. "Optimizing Daily Service Scheduling for Medical Diagnostic Equipment Considering Patient Satisfaction and Hospital Revenue," Sustainability, MDPI, vol. 10(9), pages 1-23, September.
    7. Zhang, Jian & Dridi, Mahjoub & El Moudni, Abdellah, 2019. "A two-level optimization model for elective surgery scheduling with downstream capacity constraints," European Journal of Operational Research, Elsevier, vol. 276(2), pages 602-613.
    8. Kamran Kianfar & Arezoo Atighehchian, 2023. "A hybrid heuristic approach to master surgery scheduling with downstream resource constraints and dividable operating room blocks," Annals of Operations Research, Springer, vol. 328(1), pages 727-754, September.
    9. Shuwan Zhu & Wenjuan Fan & Shanlin Yang & Jun Pei & Panos M. Pardalos, 2019. "Operating room planning and surgical case scheduling: a review of literature," Journal of Combinatorial Optimization, Springer, vol. 37(3), pages 757-805, April.
    10. Shuwan Zhu & Wenjuan Fan & Tongzhu Liu & Shanlin Yang & Panos M. Pardalos, 2020. "Dynamic three-stage operating room scheduling considering patient waiting time and surgical overtime costs," Journal of Combinatorial Optimization, Springer, vol. 39(1), pages 185-215, January.
    11. Huaxin Qiu & Dujuan Wang & Yanzhang Wang & Yunqiang Yin, 2019. "MRI appointment scheduling with uncertain examination time," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 62-82, January.
    12. Silva, Thiago A.O. & de Souza, Mauricio C., 2020. "Surgical scheduling under uncertainty by approximate dynamic programming," Omega, Elsevier, vol. 95(C).
    13. 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.
    14. Thomas Schneider, A.J. & Theresia van Essen, J. & Carlier, Mijke & Hans, Erwin W., 2020. "Scheduling surgery groups considering multiple downstream resources," European Journal of Operational Research, Elsevier, vol. 282(2), pages 741-752.
    15. Javiera Barrera & Rodrigo A. Carrasco & Susana Mondschein & Gianpiero Canessa & David Rojas-Zalazar, 2020. "Operating room scheduling under waiting time constraints: the Chilean GES plan," Annals of Operations Research, Springer, vol. 286(1), pages 501-527, March.
    16. Roshanaei, Vahid & Luong, Curtiss & Aleman, Dionne M. & Urbach, David, 2017. "Propagating logic-based Benders’ decomposition approaches for distributed operating room scheduling," European Journal of Operational Research, Elsevier, vol. 257(2), pages 439-455.
    17. Yasin Gocgun, 2018. "Simulation-based approximate policy iteration for dynamic patient scheduling for radiation therapy," Health Care Management Science, Springer, vol. 21(3), pages 317-325, September.
    18. 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.
    19. 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.
    20. Antoine Sauré & Jonathan Patrick & Martin L. Puterman, 2015. "Simulation-Based Approximate Policy Iteration with Generalized Logistic Functions," INFORMS Journal on Computing, INFORMS, vol. 27(3), pages 579-595, August.
    21. Ma, Xin & Zhao, Xue & Guo, Pengfei, 2022. "Cope with the COVID-19 pandemic: Dynamic bed allocation and patient subsidization in a public healthcare system," International Journal of Production Economics, Elsevier, vol. 243(C).
    22. Gökalp, E. & Gülpınar, N. & Doan, X.V., 2023. "Dynamic surgery management under uncertainty," European Journal of Operational Research, Elsevier, vol. 309(2), pages 832-844.

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