Daily Operating Rooms Scheduling under Uncertainty using Simulation based Optimization Approach

Document Type : Original Article


1 MA. Isfahan University.

2 Assistant Professor, University of Isfahan.


Operating room scheduling has an important role in increasing the productivity of operating rooms and reducing the hospital costs. Due to uncertainties in operating room activities, this problem can be very challenging. In this article, scheduling of daily surgical cases in 3 stages of operation, preparation, surgery and recovery, considering real constraints of teaching hospitals is investigated.  This problem, determines the surgeries sequence and start times and resource allocated to each stage of surgeries by the objectives of minimizing the cost of operating rooms’ over time and idle time under uncertainty in surgeries’ durations. Considering the inefficiency of exact methods in solving large stochastic problems, in this article a simulation based optimization approach is proposed to tackle uncertainty. A two stage ant colony optimization algorithm is combined with a simulation. The proposed algorithm is evaluated through solving several real problems from Hashemi Nejad hospital, a teaching hospital in Tehran, using a set of randomly generated scenarios. The results show the efficiency of the proposed algorithm in solving real life problems.  


1. Adeli, M. & Zandieh, M. (1392). A multi-objective simulation optimization approach for integrated supplier selection and inventory decisions. Journal of Industrial Management Perspective, 11, 89-110.
2. Atighehchian, A. (1390). Surgical case scheduling with uncertain duration of surgery. Ph.D. thesis, Department of Industrial Engineering, Factually of Engineering, Tarbiat Modares University.
3. Baesler, F., Gatica, J. & Correa, R. (2015). Simulation optimization for operating room scheduling, Int j simul model14 (2), 215-226.
4. Banditori, C., Cappanera, P. & Visintin, F. (2013). A combined optimization–simulation approach to the master surgical scheduling problem. IMA Journal of Management Mathematics24(2), 155–187.
5. BIRGE, J. R. & LOUVEAUX, F. (1997). Introduction to stochastic programming, New York, Springer.
6. Cardoen, B., Demeulemeester, E. & Beliën, J. (2010). Operating room planning and scheduling: A literature review. European Journal of Operational Research201(3), 921–932.
7. Chow, V. S., Puterman, M. L., Salehirad, N., Huang, W. & Atkins, D. (2011). Reducing Surgical Ward Congestion Through Improved Surgical Scheduling and Uncapacitated Simulation. Production and Operations Management20(3), 418–430.
8. Denton, B. T., Miller, A. J., Balasubramanian, H. J. & Huschka, T .R. (2009) Optimal Allocation of Surgery Blocks to Operating Rooms Under Uncertainty. Operations Research. 58 (4).
9. Dorigo, M., Maniezzo, V. & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
10. Eskandari, H. & Bahrami, M. (1396). Multi objective operating room scheduling using simulation based optimization. Journal of industrial engineering, 51(1), 1-13.
11. Ghazalbash, S., Sepehri, M. M., Shadpour, P. & Atighehchian, A. (2012). Operating Room Scheduling in Teaching Hospitals. Advances in Operations Research.
12. Granja, C., Almada-Lobo, B., Janela, F., Seabra, J. & Mendes, A. (2014). An optimization based on simulation approach to the patient admission scheduling problem using a linear programing algorithm. Journal of Biomedical Informatics52, 427–437.
13. JUAN, A. A., FAULIN, J., GRASMAN, S. E., RABE, M. & FIGUEIRA, G. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2,  62–72.
14. Liang, F., Guo, Y. & Fung, R. Y. (2015). Simulation-Based Optimization for Surgery Scheduling in Operation Theatre Management Using Response Surface Method. J Med Syst39(11), 159.
15. López, J., López, C., Olguín, J., Camargo, C. & López, J. (2013). Surgery Scheduling Using Simulation with Arena. Paper presented at the Proceedings of World Academy of Science, Engineering and Technology.
16. Mirghaderi, S. H. & Zandieh, M. (1390). Designing a new meta-heuristic algorithm based on the behavior of the mathematical functions xCos(x), tanh(x). Journal of Industrial Management Perspective, 2, 107-123.
17. Ozcan, Y. A., Tanfani, E. & Testi, A. (2016). Improving the performance of surgery-based clinical pathways: a simulation-optimization approach. Health Care Management Science, 20, 1, 1-15.
18. Sagnol, G., Barner, C., Borndörfer, R., Grima, M., Seeling, M., Spies, C. & Wernecke, K. (2016). Robust Allocation of Operating Rooms: a Cutting Plane Approach to handle Lognormal Case Durations and Emergency Arrivals. ZIB Report ,16-18
19. Samudra, M., Van Riet, C., Demeulemeester, E., Cardoen, B., Vansteenkiste, N. & Rademakers, F. E. (2016). Scheduling operating rooms: achievements, challenges and pitfalls. Journal of Scheduling19(5), 493–525.
20. Saremi, A., Jula, P., ElMekkawy, T. & Wang, G. G. (2013). Appointment scheduling of outpatient surgical services in a multistage operating room department. International Journal of Production Economics141(2), 646–658.
21. Sun, Y. & Li, X. (2011). Optimizing surgery start times for a single operating room via simulation. Proceedings of the 2011 Winter Simulation Conference (WSC).
22. Tarkesh, H., Atighehchian, A, & Nookabadi, A. S.( 2009).Facility layout design using virtual multi-agent system. Journal of Intelligent Manufacturing20(4), 347-357 .
23. Xiang, W., Yin, J. & Lim, G. (2015). An ant colony optimization approach for solving an operating room surgery scheduling problem. Computers & Industrial Engineering85, 335–345.
24. Zhang, Z., Xie, X. & Geng, N. (2012). Promise surgery start times and implementation strategies. 2012 IEEE International Conference on Automation Science and Engineering (CASE).
25. Zhang, Z., Xie, X. & Geng, N. (2014). Simulation-based surgery appointment sequencing and scheduling of multiple operating rooms. In 2014 IEEE International Conference on Automation Science and Engineering (CASE) (pp. 399–404). IEEE.