A Mathematical Model for Multi-Period Surgical Scheduling with Capacity Constraint
https://doi.org/10.56225/ijgoia.v1i3.70
Keywords:
Mathematical Model, Surgical Scheduling, Multi-period, Capacity Constraint, MinimizationAbstract
This study proposes a mathematical model for multi-period surgical scheduling problem with capacity constraint over a time horizon. The goal is to schedule a list of patients who must undergo various kinds of operations by different eligible hospitals. In particular, each operation must be performed in a particular time period and different operations of one patient can be performed by different eligible hospitals. In addition, each hospital has limited surgery capacity for each time period. The problem is formulated with a multi-objective model using the weighted sum approach of two objectives: minimization of makes pan and minimization of total least preference assignment score. The experiment is executed using the simulated data according to the real treatments of cleft lip and palate patients. The results show that the model yield the correct assignment and operation sequence respected to all constraints. Thus, this proposed mathematical model can be further used as smart decision tool in surgical scheduling in hospital network.
Downloads
References
Cardoen, B., Demeulemeester, E., & Beliën, J. (2010). Operating room planning and scheduling: A literature review. European Journal of Operational Research, 201(3), 921–932. https://doi.org/10.1016/j.ejor.2009.04.011
Donahue, R., Russell, D., de Riese, C., Smith, C., de Riese, W. T. W., & Medway, A. (2017). Patients willing to wait: arrival time, wait time and patient satisfaction in an ambulatory urology clinic. Urology Practice, 4(1), 1–6. https://doi.org/10.1016/j.urpr.2016.02.003
Drupsteen, J., van der Vaart, T., & van Donk, D. P. (2013). Integrative practices in hospitals and their impact on patient flow. International Journal of Operations & Production Management, 33(7), 912–933. https://doi.org/10.1108/IJOPM-12-2011-0487
Gartner, D., & Kolisch, R. (2014). Scheduling the hospital-wide flow of elective patients. European Journal of Operational Research, 233(3), 689–699. https://doi.org/10.1016/j.ejor.2013.08.026
Hamid, M., Nasiri, M. M., Werner, F., Sheikhahmadi, F., & Zhalechian, M. (2019). Operating room scheduling by considering the decision-making styles of surgical team members: a comprehensive approach. Computers & Operations Research, 108, 166–181. https://doi.org/10.1016/j.cor.2019.04.010
Langer, E. J. (1977). The psychology of chance. Journal for the Theory of Social Behaviour, 7(2), 185–207.
May, J. H., Spangler, W. E., Strum, D. P., & Vargas, L. G. (2011). The surgical scheduling problem: Current research and future opportunities. Production and Operations Management, 20(3), 392–405. https://doi.org/10.1111/j.1937-5956.2011.01221.x
Min, D., & Yih, Y. (2010). An elective surgery scheduling problem considering patient priority. Computers & Operations Research, 37(6), 1091–1099. https://doi.org/10.1016/j.cor.2009.09.016
Silva, T. A. O., & de Souza, M. C. (2020). Surgical scheduling under uncertainty by approximate dynamic programming. Omega, 95, 102066. https://doi.org/10.1016/j.omega.2019.05.002
Vissers, J. M. H., Adan, I. J. B. F., & Bekkers, J. A. (2005). Patient mix optimization in tactical cardiothoracic surgery planning: a case study. IMA Journal of Management Mathematics, 16(3), 281–304. https://doi.org/10.1093/imaman/dpi023
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.