Journal of Applied Science and Engineering

Published by Tamkang University Press


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Innaka Rizki Meliana1, Pasca Purwoko1, Azizah Aisyati1, Yusuf Priyandari1, and Cucuk Nur Rosyidi This email address is being protected from spambots. You need JavaScript enabled to view it.1

1Industrial Engineering Department, Universitas Sebelas Maret (Surakarta, Indonesia)


Received: July 22, 2021
Accepted: November 4, 2021
Publication Date: December 17, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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Department of Surgery (DOS) contributes to a major portion of hospital’s expenditures and revenues. The costs come from resources which are involved in a surgery. These resources must be used optimally through proper scheduling. Recently, there are still many hospitals schedule the operating rooms manually which will take relatively longer time. Whereas the scheduling of the operating room can be done through developing a mathematical model. We consider several aspects in the model of this research, including the number of operating rooms, working hours, the available number of anesthetic machines, and utilization of each room. We propose a mixed integer linear programming (MILP) model to solve the operating room scheduling problem and based on the model, a decision support system (DSS) is developed to allow the hospital generates a better schedule with a shorter time. The results of sensitivity shows that the model is sensitive to the changes of surgery duration and the available number of anesthetic machines.

Keywords: Decision support system; Mixed Integer Linear Programming; Operating rooms; Scheduling


  1. [1] B. Denton, J. Viapiano, and A. Vogl, (2007) “Optimization of surgery sequencing and scheduling decisions under uncertainty" Health care management science 10(1):13–24. DOI: 10.1007/s10729-006-9005-4.
  2. [2] T. Khaniyev, E. Kayı¸s, and R. Güllü, (2020) “Next-day operating room scheduling with uncertain surgery durations: Exact analysis and heuristics" European Journal of Operational Research 286(1): 49–62. DOI: 10.1016/j.ejor.2020.03.002.
  3. [3] P. Patterson, (1996) “What makes a well-oiled scheduling system?" OR manager 12(9): 19–23.
  4. [4] C. A. Oktavia, R. Rahmadwati, and P. B. Santoso, (2016) “Analisis Kinerja Algoritma C4. 5 Pada Sistem Pendukung Keputusan Penentuan Jenis Pelatihan" Jurnal EECCIS 9(2): 144–149.
  5. [5] S. Zanda, P. Zuddas, and C. Seatzu, (2018) “Long term nurse scheduling via a decision support system based on linear integer programming: A case study at the University Hospital in Cagliari" Computers & Industrial Engineering 126: 337–347. DOI: 10.1016/j.cie.2018.09.027.
  6. [6] M. M. Seref, R. K. Ahuja, and W. L. Winston. Developing spreadsheet-based decision support systems. Dynamic Ideas, 2007.
  7. [7] D. A. Savi´c, J. Bicik, and M. S. Morley, (2011) “A DSS generator for multiobjective optimisation of spreadsheetbased models" Environmental modelling & software 26(5): 551–561. DOI: 10.1016/j.envsoft.2010.11.004.
  8. [8] G. Sinha. “Modern Optimization Methods for Science, Engineering and Technology.” In: Myanmar: IOP Science. 2019.
  9. [9] I. Marques, M. E. Captivo, and M. V. Pato, (2012) “An integer programming approach to elective surgery scheduling" OR spectrum 34(2): 407–427. DOI: 10.1007/s00291-011-0279-7.
  10. [10] A. Jeang and A.-J. Chiang, (2012) “Economic and quality scheduling for effective utilization of operating rooms" Journal of medical systems 36(3): 1205–1222. DOI:10.1007/s10916-010-9582-0.
  11. [11] H. Saadouli, M. Masmoudi, B. Jerbi, and A. Dammak. “An optimization and Simulation approach for Operating Room scheduling under stochastic durations”. In: 2014 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE. 2014, 257–262. DOI: 10.1109/CoDIT.2014.6996903.
  12. [12] A. Abedini, W. Li, and H. Ye, (2017) “An optimization model for operating room scheduling to reduce blocking across the perioperative process" Procedia Manufacturing 10: 60–70. DOI: 10.1016/j.promfg.2017.07.022.
  13. [13] M. Hamid, R. Tavakkoli-Moghaddam, B. Vahedi-Nouri, and H. Arbabi, (2020) “A mathematical model for integrated operating room and surgical member scheduling considering lunch break" International Journal of Research in Industrial Engineering 9(4): 304–312.
  14. [14] M. Dios, J. M. Molina-Pariente, V. Fernandez-Viagas, J. L. Andrade-Pineda, and J. M. Framinan, (2015) “A decision support system for operating room scheduling" Computers & Industrial Engineering 88: 430–443. DOI: 10.1016/j.cie.2015.08.001.
  15. [15] M. G. Güler and E. Geçici, (2020) “A decision support system for scheduling the shifts of physicians during COVID-19 pandemic" Computers & Industrial Engineering 150: 106874. DOI: 10.1016/j.cie.2020.106874.
  16. [16] E. Turban, J. E. Aronson, and T. P. Liang, (2005) “Decision Support Systems and Intelligent System,(Sistem Pendukung Keputusan dan Sistem Cerdas) Ed. 7. Jld. 2":
  17. [17] A. J. Mason, (2013) “SolverStudio: A new tool for better optimisation and simulation modelling in Excel" INFORMS Transactions on Education 14(1): 45–52.
  18. [18] W. E. Hart, C. D. Laird, J.-P. Watson, D. L. Woodruff, G. A. Hackebeil, B. L. Nicholson, J. D. Siirola, et al. Pyomo-optimization modeling in python. 67. Springer, 2017.



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