Abstract
In this paper, we address the multiple operating room (OR) surgical case sequencing problem (SCSP). The objective is to maximise total OR utilisation during standard opening hours. This work uses a case study of a large Australian public hospital with long surgical waiting lists and high levels of non-elective demand. Due to the complexity of the SCSP and the size of the instances considered herein, heuristic techniques are required to solve the problem. We present constructive heuristics based on both a modified block scheduling policy and an open scheduling policy. A number of real-time reactive strategies are presented that can be used to maintain schedule feasibility in the case of disruptions. Results of computational experiments show that this approach maintains schedule feasibility in real-time, whilst increasing operating theatre (OT) utilisation and throughput, and reducing the waiting time of non-elective patients. The framework presented here is applicable to the real-life scheduling of OT departments, and we provide recommendations regarding implementation of the approach.
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Notes
Note: the OT is the set of ORs.
References
Bruni ME, Beraldi P, Conforti D (2015) A stochastic programming approach for operating theatre scheduling under uncertainty. IMA J Manag Math 26(1):99–119. https://doi.org/10.1093/imaman/dpt027
Cardoen B, Demeulemeester E, Beliën J (2009) Optimizing a multiple objective surgical case sequencing problem. Int J Prod Econ 119(2):354–366. https://doi.org/10.1016/j.ijpe.2009.03.009
Cardoen B, Demeulemeester E, Van der Hoeven J (2010) On the use of planning models in the operating theatre: results of a survey in Flanders. Int J Health Plan Manag 25(4):400–414. https://doi.org/10.1002/hpm.1027
Duma D, Aringhieri R (2015) An online optimization approach for the real time management of operating rooms. Oper Res Health Care 7:40–51. https://doi.org/10.1016/j.orhc.2015.08.006
Duma D, Aringhieri R (2018) The real time management of operating rooms. In: Kahraman C, Topcu YI (eds) Operations research applications in health care management. Springer, Cham, pp 55–79
Duma D, Aringhieri R (2019) The management of non-elective patients: shared vs. dedicated policies. Omega 83:199–212. https://doi.org/10.1016/j.omega.2018.03.002
Ferrand YB, Magazine MJ, Rao US (2014) Managing operating room efficiency and responsiveness for emergency and elective surgeries—a literature survey. IIE Trans Healthc Syst Eng 4(1):49–64. https://doi.org/10.1080/19488300.2014.881440
Gurobi Optimization, LLC (2019) Gurobi optimizer reference manual. http://www.gurobi.com
Heydari M, Soudi A (2016) Predictive/reactive planning and scheduling of a surgical suite with emergency patient arrival. J Med Syst 40(30):1–9. https://doi.org/10.1007/s10916-015-0385-1
Samudra M, Van Riet C, Demeulemeester E, Cardoen B, Vansteenkiste N, Rademakers FE (2016) Scheduling operating rooms: achievements, challenges and pitfalls. J Sched. https://doi.org/10.1007/s10951-016-0489-6
Spratt B, Kozan E (2018) Operating theatre data. Retrieved from Mendeley data. https://doi.org/10.17632/sccbx9jzp5.1
Spratt B, Kozan E, Sinnott M (2018) Analysis of uncertainty in the surgical department: durations, requests, and cancellations. Aust Health Rev. https://doi.org/10.1071/AH18082
Stuart K, Kozan E (2012) Reactive scheduling model for the operating theatre. Flex Serv Manuf J 24(4):400–421. https://doi.org/10.1007/s10696-011-9111-6
Van Riet C, Demeulemeester E (2015) Trade-offs in operating room planning for electives and emergencies: a review. Oper Res Health Care 7:52–69. https://doi.org/10.1016/j.orhc.2015.05.005
Wang B, Han X, Zhang X, Zhang S (2015) Predictive–reactive scheduling for single surgical suite subject to random emergency surgery. J Combin Optim 30(4):949–966. https://doi.org/10.1007/s10878-015-9861-2
Acknowledgements
This research was funded by the Australian Research Council (ARC) Linkage Grant LP 140100394. Computational resources and services used in this work were provided by the High Performance Computing and Research Support Group, Queensland University of Technology, Brisbane, Australia.
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Spratt, B., Kozan, E. A real-time reactive framework for the surgical case sequencing problem. Flex Serv Manuf J 33, 183–211 (2021). https://doi.org/10.1007/s10696-019-09371-6
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DOI: https://doi.org/10.1007/s10696-019-09371-6