Proposing a Model for Analyzing and Improving a Service System through Queue Theory and Simulation Approach (Case: Hamedan Power Company)

Document Type : Original Article


1 Master’s Degree Graduated, Allameh Tabatabai University.

2 Assistant Professor, Bu Ali Sina University.

3 Associate Professor, Islamic Azad University, Qazvin Branch.


It’s evident that waiting in a queue is not desirable. Nevertheless, reducing the waiting time will be costly. In order to enhance the efficiency and improve the performance of a system, there are some solutions that result in reductions in the response time and enhancement in user satisfaction. Among all, the simulation approach does not deliver a real optimal solution but provides a description of the events that take place under certain conditions in the system. Through such a model, the decision-maker can investigate system improvements via scenario analysis. This study aims to analyze a system’s behavior through queue modeling, simulation, and statistical analysis. The case under study was a service system i.e. the financial department of Hamedan Power Company. This system was modeled and analyzed via the ED software, version 8.1. Thereby, improving changes were foreseen and statistically analyzed. Findings on the proposed scenario show a significant reduction in the total waiting time of this system. Based on this scenario, it was proposed that three personnel – via a training program – serve all the three customer types (A, B, & C). In this way, the model format changed. Almost 60 seconds of each customer’s time was saved thereby. Hence the workflow can be changed through interventions such as developing some training programs.


Main Subjects

  1. Abedi, S., Radfar. R., & Hamidi, N. (2010). Optimization of fuel station deployment plan by using simulation tools in queue theory. Development and transformation management, 2(4), 43- 52.
  2. Aeenparast, A., Tabibi, S. J., Shahanaghi, K., & Aryanejhad, M. B. (2013). Reducing outpatient waiting time: a simulation modeling approach. Iranian Red Crescent Medical Journal15(9), 865.
  3. A Kazemi. M.A., Dad, A. (2008).Improve factory production line layout using queuing systems simulation. Islamic Azad University Tehran Branch. MA. Page 24.
  4. Akbari Haghighinejad, H. A., Kharazmi, E., Hatam, N., Yousefi, S., Hesami, S. A., Danaei, M., & Askarian, M. (2016). Using queuing theory and simulation modelling to reduce waiting times in an Iranian emergency department. International journal of community based nursing and midwifery, 4(1), 11. (In Persian).
  5. Alvani, M., Jandaghi & G., Safari, M. (2011). Evaluation of bank branches and the factors  influencing  it  (Case  study  of  Tehran  branch  of  Bank  Sepah). Journal of Public Administration University of Tehran, 4(3), 1-18.
  6. Asgharizadeh, E., Ahmadi, S. H., & Yousefi Dehbidi, Sh. (2012). Prioritizing Quality Aspects in Service Organizations: A Comparision between Compensatory and Noncompensatory Method. Journal of Industrial Management Perspective, 4(4), 107-122 (In Persian).
  7. Asgharizade, E., Bitaraf, A., Ajali, M. (2011).Providing a hybrid model using fuzzy PROMETHEE and multi-objective linear programming for outsourcing warranty services. Journal of Industrial Management Perspective, 1(2), 43-60 (In Persian).
  8. Atighechiyan, A., & Emanpoor, M. (2017). Daily scheduling of operating rooms in conditions of uncertainty with a simulation-based optimization approach. Journal of Industrial Management Perspective, 7(27), 82-53 (In Persian).
  9. Arkat, J. H., farhani, M. (2010). Investigating the effectiveness of service restructuring in banks using queue models. 7th International Conference on Industrial Engineering, Isfahan, Iranian Industrial Engineering Association, Isfahan University of Technology. (In Persian).

10. Azar, A., Mohammadlo, m., Moghbal Ba Arz, A., & Ahmady, P. (2012). Designing a Framework to measure the quality of service in the supply chain. Journal of Industrial Management Perspective, 6(2), 9-24. (In Persian)

11. Azimi, P., & Ghanbari, M.R. (2015). Optimization of grain material transportation based on a simulation model in Shahid Rajaei port. Journal of Industrial Management Studies, 13(38), 133-161. (In Persian).

12. Azimi, P. (2013). Simulation via Optimization by ED Education Software. Qazvin Azad University Press, Pp. 47-50. (In Persian).

13. Barrer, D.Y. (1957). Queuing with impatient customers and ordered Service. Operations Research, 5(5), 650-656.

14. Beier, G. (1997). Optimal personnel configuration of branch office banking through applied queueing network theory. In Operations Research Proceedings 1996 (pp. 157-162). Springer, Berlin, Heidelberg.

15. Bouazzi, I., Bhar, J., & Atri, M. (2017). Priority-based queuing and transmission rate management using a fuzzy logic controller in WSNs. ICT Express, 3(2), 101-105.

16. Cascone, A., Rarità, L., & Trapel, E. (2014). Simulation and Analysis of a Bank’s Multi-Server Queueing System. Journal of Mathematical Sciences196(1), 23-29.

17. Choobin, B., & Hoosseyni, J. (2016). Container terminal simulation and equipment optimization with ED software; The first conference on management, entrepreneurship, marketing, economic development, insurance and crisis management with the approach of sustainable development and passive defense, electronically. Iran Conference Management Institute, Center for Passive Defense Studies.

18. Choudhury, A. & Medhi, P. (2010). A Simple Analysis of Customers Impatience in Multiserver Queues. Journal of Applied Quantitative Methods, 5(2), 182-197. 

19. Chung, Ch. A. (2003). Simulation modeling handbook: a practicalapproach. CRC press, Inc. Boca Raton, FL, USA, ISBN 0-8493-1241-8.

20. Fodor, G., Blaabjerg, S., Andersen A. (1998). Modeling and simulation of mixed queueing and loss systems. Wireless Personal Communications, 8(3), 253 276.

21. Gautam, N. (2002). Performance analysis and optimization of web proxy servers and mirror sites. European Journal of Operational Research, 142, 396–418.

22. Gou, X., Xu, Z. & Liao, H. (2017). Hesitant fuzzy linguistic entropy and cross-entropy measures and alternative queuing method for multiple criteria decision making. Information Sciences, 388, 225-246.

23. Gross, D. (2008). Fundamentals of queueing theory. John Wiley & Sons, USA.

24. Gupta, M. B. & Khanna, R. B. (2006). Quantitative Techniques for Decision Making.2nd Edition, New Delhi: Prentice Hall of India.

25. Hagighi, A., Montazer, Medhi, J. & Mohanty, S. G. (1986). On a multi server Markovian queuing system with Balking and Reneging. Computer and Operational Research, 13(4), 421-425.

26. Jafarnejad, A. Mohseni, M. Abdollahi, A. (2014). Proposing a Hybrid Fuzzy PROMETHEE - AHP Approach to Performance Evaluation of Service Supply Chain (Case Study: Hotel industry). Journal of Industrial Management Perspective, 4 (14), 69-92. (In Persian)

27. Jin Y.S., Ming X., Li X., Wen J.Y., & Jin D. (2009). Customer-centric optima resource reconfiguration for service outlet. International Conference ofService Operations, Logistics and Informatics. Pp. 754-759.

28. Kazemi, M., Sibuyeh, A., Ranjbar, M., Najiazimi, Z., Karimi, Z. (2014). Simulation, system, emergency unit, and ranking of scenarios, improvement, using the PROMETHEE-AHP method. Journal of Industrial Management Perspective12, 137-164 (In Persian).

29. Kleinrock, L. (1975). Queueing Systems. A Wiley-Interscience Publication, Volume I.

30. Kleinrock, L. (1975). Queueing Systems. Vol. I: Theory. Wiley, New York

31. Little, John DC. “A proof for the queuing formula: L= λ W.” Operations research 9.3 (1961): 383-387.

32. Madadi, N., Haghighian Roudsari, A., Yew Wong, K., & Rahiminezhad Galankashi, M. (2013). Modeling and Simulation of a Bank Queuing System. University Teknologi Malaysia.

33. Mehdiniya, Sh., Varshooei, P., Janatipoor, M., Shirazi, B., Mahdavi, E. (2012). Improving the parameter of estimating customer waiting time in bank queuing systems using discrete event simulation with process-oriented approach. 8th International Conference on Industrial Engineering, Tehran, Iran Industrial Engineering Association, Amir Kabir University of Technology.

34. Mesgari, F., Bagherinezhad, J. (2013). Simulation of the bank queue system by ARENA software and analysis of its performance criteria. First National Conference on Accounting and Management, Shiraz, Kharazmi International Educational and Research Institute.

35. Mirzabaghi, M., & Jolai, F. (2017). Inventory scheduling in a multi-supplier G / G / 1 /∞ queue system using response level simulation and methodology; Journal of Industrial Management Perspective, 27, 9-26 (In Persian).

36. Mohammadlu, M., Hamidi, N., Hajkarimi, B. (2011). Electronic banking and queue density of bank counters (Case study of queue criteria in traditional and electronic banking). Productivity management (beyond management), 5(17), 161-190.

37. Momeni, M., Mohghar, A., Matinnafs, F. (2006). Assessing the performance of the employee-delivery queue system in Sepah Bank. Management Knowledge Quarterly, 74, 111-131.

38. Moradi, H., Rezaei, A., Bagherinezhad, J. (2017). Study and analysis of the function of Kian Airlines Agency system using software. First International Conference on Systems Optimization and Business Management, Babol, Noshirvani University of Technology - Iranian Association for Operations Research.

39. Mousakhani, M., Haghighi, M., & Torkzadeh, S. (2012). Model to gain customer loyalty through customer knowledge management in the banking industry. Journal of Business Management, 4(12), 147-164.

40. Nong, Ye, Esma, S., Gel, Xueping Li, Toni Farley, Ying-Cheng Lai, (2005). Web server QoS models: applying scheduling rules from production planning. Computers & Operations Research, 32, 1147– 1164.

41. Parimala Sree, R., & Palaniammal, S. (2014). Application of Queueing Theory in Bank Sectors. International Journal ofDevelopment Research, 4(12), 2783-2789.

42. Rayatpishe, S. Tiztoo, A. (2016). Strategie’s of Customs and Logistics to Clients through Scenario Planning Approach. Journal of Industrial Management Perspective, 6(23)101-129. (In Persian)

43. Render, B., Stair, M., & Hanna, E. (2007). Quantitative AnalysisManagement 9nd edition. New Delhi: Prentice Hall of India.

44. Rezaei Pandari, A., & Azar, A. (2017). A fuzzy cognitive mapping model for service supply chains performance. Measuring Business Excellence, 21(4), 388-404. (In Persian)

45. Rodríguez-Sanz, Á., de Marcos, A. F., Pérez-Castán, J. A., Comendador, F. G., Valdés, R. A., & Loreiro, Á. P. (2021). Queue behavioural patterns for passengers at airport terminals: A machine learning approach. Journal of Air Transport Management90, 101940.

46. Ross, S. M. (1997). Introduction to probability models. 6th ed., Academic Press, London.

47. Rossetti, M.D., Hill, R.R., Johansson, B., Dunkin, A., & Ingalls, R.G. (2009). A  Brief Introduction to Optimization via Simulation. Proceedings of the 2009 Winter Simulation Conference, 75-85.

48. Saadatjo, F. (2016). Analysis, modeling and simulation of the queue system of Yazd Central Laboratory using ED software.

49. Sheykhani, S. (2007). Electronic banking and strategies in Iran. Institute for Monetary and Banking Studies, Pp. 32-33.

50. Shannon, R. E. (1975). Systems Simulation: The Art and Science.

51. Taghavifard, M.T., Dadvand, A., Aghaei, M. (2018). Improving the service process and reducing the waiting time of customers in the bank with a simulation approach. Quarterly Journal of Intelligent Business Management Studies, 6(22), 75-105.

52. Taghizadeh Yazdi, M.R, Sabzali Rezaei, S, Emamat, M.M, Alikhani, H. (2018). Evaluating Service Quality of Airlines using a Hybrid Fuzzy MADM Approach. Journal of Industrial Management Perspective, 8(2), 135-164. (In Persian)

53. Teo, Y.M., & Ayani, R. Comparison of load balancing strategies on cluster-based web servers. The International Journal of the Society for Modeling and Simulation, 77(6), 185–195, 2000.

54. Vaghefzadh, M.H., Karimi, B. (2018). Demand Management using Autoregressive-Time Series Modeling in Mobile Value-Added Services. Journal of Industrial Management Perspective, 8(30), 9-30. (In Persian)