Discrete Event Simulation and Data Envelopment Analysis to Improve the Performance of Hospital Emergency Department

Document Type : Research Paper


1 Assistant Prof., Department of Industrial Engineering, Fouman Faculty of Engineering, College of Engineering, University of Tehran, Iran

2 MSc., Department of Industrial Engineering, College of Engineering, Campus Technical Schools, University of Tehran, Tehran, Iran

3 Associate Prof., Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran


Objective: This study applies a discrete event simulation to improve the performance of hospital emergency department in order to reduce the waiting time for patients and optimize the resources.
Methods: First, a simulation model is developed based on flow of patients in an emergency department using ARENA software; then, the simulation model is run 180 times for any feasible scenario including some physicians, emergency medicine specialists, nurses, acute phase cure unit, injection unit, supervised ward, and ICU unit. In the next step, two data envelopment analysis methods are used for ranking the scenarios.
Results: Ranking of DEA methods showed that the scenario number 39 is the best choice in both methods. Non-parametric Spearman-Row and Kendal-Tau tests were used to determine the correlation among the results of ranking methods. The results of the two tests (0.93 and 0.81, respectively) indicated a significant correlation among DEA ranking methods.
Conclusion: The results of case study showed that the scenario 39 is the best scenario among all the 44 feasible scenarios defined in both DEA methods; that is, there should be 2 general physicians, 1 emergency medicine specialist, 16 nurses in the supervised ward, 5 nurses in the acute care unit, 2 beds in the injection room, 22 beds in the supervised ward and 16 beds in the acute care unit.


Al-Refaie, A., Fouad, R. H., Li, M.-H., and Shurrab, M. (2014). Applying simulation and DEA to improve performance of emergency department in a Jordanian hospital. Simulation Modelling Practice and Theory, 41, 59-72.
Amaral, T. M., and Costa, A. P. C. (2014). Improving decision-making and management of hospital resources: An application of the PROMETHEE II method in an Emergency Department. Operations Research for Health Care, 3(1), 1-6.
Andersen, P., and Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management science, 39(10), 1261-1264.
Azadeh, A., Ahvazi, M. P., Haghighii, S. M., and Keramati, A. (2016). Simulation optimization of an emergency department by modeling human errors. Simulation Modelling Practice and Theory, 67, 117-136.
Azadeh, A., Ghaderi, S. F., Miran, Y. P., Ebrahimipour, V., and Suzuki, K. (2007). An integrated framework for continuous assessment and improvement of manufacturing systems. Applied Mathematics and Computation, 186(2), 1216-1233.
Azadeh, A., Tohidi, H., Zarrin, M., Pashapour, S., and Moghaddam, M. (2016). An integrated algorithm for performance optimization of neurosurgical ICUs. Expert Systems with Applications, 43, 142-153.
Banker, R. D., Charnes, A., and Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.
Baratloo, A., Rahmati, F., Forouzanfar, M. M., Hashemi, B., Motamedi, M., & Safari, S. (2015). Evaluation of performance indexes of emergency department. Iranian Journal of Emergency Medicine, 2(1), 33-38. (in Persian)
Charnes, A., Cooper, W. W., and Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
Cochran, J. K., and Roche, K. T. (2009). A multi-class queuing network analysis methodology for improving hospital emergency department performance. Computers & Operations Research, 36(5), 1497-1512.
Cooper, W. W., Seiford, L. M., and Tone, K. (2006). Introduction to data envelopment analysis and its uses: with DEA-solver software and references, Springer Science & Business Media.
Elalouf, A., and Wachtel, G. (2015). An alternative scheduling approach for improving patient-flow in emergency departments. Operations Research for Health Care, 7, 94-102.
Farzaneh Kholghabad, H., Alisoltani, N., Nazari-Shirkouhi, S., Azadeh, M., & Moosakhani, S. (2019). A Unique Mathematical Framework for Optimizing Patient Satisfaction in Emergency Departments. Iranian Journal of Management Studies, 12(2), 81-105.
Guide, M. U. S. (2000). Data analysis and quality tools User's Guide 2. Minitab Inc.
Gul, M., and Guneri, A. F. (2015). A comprehensive review of emergency department simulation applications for normal and disaster conditions. Computers & Industrial Engineering, 83, 327-344.
Kelton, W. D. (2007). Simulation with ARENA, McGraw-hill.
Konrad, R., DeSotto, K., Grocela, A., McAuley, P., Wang, J., Lyons, J., and Bruin, M. (2013). Modeling the impact of changing patient flow processes in an emergency department: Insights from a computer simulation study. Operations Research for Health Care, 2(4), 66-74.
Kuo, Y. H., Leung, J. M., Graham, C. A., Tsoi, K. K., & Meng, H. M. (2018). Using simulation to assess the impacts of the adoption of a fast-track system for hospital emergency services. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 12(3), JAMDSM0073-JAMDSM0073.
Liu, Z., Rexachs, D., Epelde, F., and Luque, E. (2017). A simulation and optimization based method for calibrating agent-based emergency department models under data scarcity. Computers & Industrial Engineering, 103, 300-309.
Mielczarek, B. e. (2014). Simulation modelling for contracting hospital emergency services at the regional level. European Journal of Operational Research, 235(1), 287-299.
Oh, C., Novotny, A. M., Carter, P. L., Ready, R. K., Campbell, D. D., and Leckie, M. C. (2016). Use of a simulation-based decision support tool to improve emergency department throughput. Operations Research for Health Care, 9, 29-39.
Pegden, C. D. (1984). Introduction to SIMAN. In Proceedings of the 16th conference on winter simulation (pp. 34-41). IEEE Press.
Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European journal of operational research, 130(3), 498-509.
Yazdi, M. R. T., Mozaffari, M. M., Nazari-Shirkouhi, S., & Asadzadeh, S. M. (2018). Integrated Fuzzy DEA-ANFIS to Measure the Success Effect of Human Resource Spirituality. Cybernetics and Systems, 49(3), 151-169.
Zeinali, F., Mahootchi, M., and Sepehri, M. M. (2015). Resource planning in the emergency departments: A simulation-based metamodeling approach. Simulation Modelling Practice and Theory, 53, 123-138.
Zeng, Z., Ma, X., Hu, Y., Li, J., and Bryant, D. (2012). A simulation study to improve quality of care in the emergency department of a community hospital. Journal of emergency Nursing, 38(4), 322-328.