Multi-Objective Operating Room Scheduling Using Simulation-based Optimization

Document Type: Research Paper

Authors

Management and Productivity Study Center, Tarbiat Modares University, Tehran, Iran

Abstract

As the main source of income and expenses of hospitals, operating rooms (ORs) are the engines of hospitals' economics and they have a significant impact on public health. Many papers concerned regarding OR planning and scheduling problems, but they have not considerably applied the simulation-based optimization approach to solve the problems. In OR scheduling problems, there are a number of ORs and some surgeons with different specialties and each surgeon has a waiting list of some patients that each surgery should be planned and scheduled on the days when relevant surgeons are available. In this study, we consider two objectives: (1) minimizing the costs of overtime staffing and ORs’ idle time, and (2) minimizing the number of waiting days for patients. The mathematical model of OR scheduling problem is developed and solved by both exact method and simulation-based optimization approach. The comparison of results obtained from exact method and simulation-based optimization approach indicates that the exact method is only able to solve the small-size problems in reasonable time, while simulation-based optimization approach find competitive solutions for both small-size and large-size problems and solve large-size problems in an acceptable time.

Keywords

Main Subjects


  1. Denton, B., Viapiano, J. and Vogl, A. (2007). “Optimization of surgery sequencing and scheduling decisions under uncertainty”, Health Care Management Science, Vol. 10, No. 1, PP. 13–24.
  2. Litvak, E. and Long, M. (2000). “Cost and quality under managed care: Irreconcilable differences”, The American Journal of Managed Care, Vol. 6, No. 3, PP. 305–312.
  3. Ateeghechian, A. (2011). “Surgery operations scheduling with stochastic times”, PhD Thesis, Faculty of Industrial Engineering, Tarbiat Modarres University, Tehran, Iran.
  4. Cardeon, B., Demeulemeester, E. and Belien, J. (2010). “Operating room planning and scheduling: A literature review”, European Journal of Operational Research, Vol. 201, PP. 921–932.
  5. Denton, B. T., Miller, A. J., Balasubramanian, H. J. and Huschka, T. R. (2009). “Optimal Allocation of Surgery Blocks to Operating Rooms under Uncertainty”, Operations Research, Vol. 58, No.4, PP. 802–816.
  6. Arnaout, J. P. M. and Kulbashian, S. (2008). “Maximizing the utilization of operating rooms with stochastic times using simulation”, A Proceedings of the 2008 Winter Simulation Conference, PP. 1617–1623.
  7. Liu, Y., Chu, C. and Wang, K. (2011). “New heuristic algorithm for the operating room scheduling problem”, Computers and Industrial Engineering, Vol. 61, PP. 865–871.
  8. Cochran, J. K. and Roche, K. (2009). “A multi-class queuing network analysis methodology for improving hospital emergency department performance”, Computers and Operations Research, Vol. 36, No. 5, PP. 1497–1512.
  9. Meskens, N., Duvivier, D. and Hanset, A. (2013). “Multi-objective operating room scheduling considering desiderata of the surgical team”, Decision Support Systems, Vol. 55, PP. 650–659.
10. Lamiri, M., Grimaud, F. and XIE, X. (2009). “Optimization methods for a stochastic surgery planning problem”, Int. J. Production Economics, Vol. 120, No. 2, PP. 400–410.

11. Aringhieri, A., Landab, P., Sorianoc, P., Tànfanib, E. and Testi, A., (2015). “A two level metaheuristic for the operating room scheduling and assignment problem”, Computers and Operations Research, Vol. 54, PP. 21–34.

12. Davies, R. and Davies, H. (1994). “Modeling patient flows and resource provision in health systems”, Omega, Vol. 22, No. 2, PP. 123–131.

13. Lowery, J. C. (1998). “Getting started in simulation in health care”, Proceedings of the 1998 Winter Simulation Conference, PP. 31–35.

14. Saremi, A., Jula, P., Elmekkawy, T. and Wang, G. G. (2013). “Appointment scheduling of outpatient surgical services in a multistage operating room department”, Int. J. Production Economics, Vol. 141, No. 2, PP. 646–658.

15. M'hallah, R. and Al-roomi, A. H. (2014). “The planning and scheduling of operating rooms: A simulation approach”, Computers and Industrial Engineering, Vol. 78, PP. 235–248.

16. Persson, M. J. and Persson, J. A. (2009). “Health economic modeling to support surgery management at a Swedish hospital”, Omega, Vol. 37, No. 4, PP. 853–863.

17. Van Essen, J. T., Hans, E. W., Hurink, J. L. and Oversberg, A. (2012). “Minimizing the waiting time for emergency surgery”, Operations Research for Health Care, Vol. 1, No. 2, PP. 34–44.

18. Deb, K., Pratap, A., Agarval, S. and Meyarivan, T. (2002). “A Fast and elitist multiobjective genetic algorithm: NSGAII”, IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, PP. 182–197.

19. Eskandari, H. and Geiger, C. D. (2008). “A fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problems”, Journal of Heuristics, Vol. 14, No. 3, PP. 203–241.

20. Klemmt, A., Horn, S., Weigert, G. and Wolter, K. (2009). “Simulation-based optimization vs. mathematical programming: A hybrid approach for optimizing scheduling problems”, Robotics and Computer-Integrated Manufacturing, Vol. 25, No. 6, PP. 917–925.