Optimizing the Banking Service System Using Queue Theory, Fuzzy DEMATEL and TOPSIS Approach: Case Study

https://doi.org/10.35912/ahrmr.v2i2.1035
Published: Dec 15, 2022

Abstract:

Purpose: This paper aimed to improve and optimize the overall performance of the banking service system using queue theory in various activities whilst maximizing profits.

Research Methodology: Based on previous literature and interview with related experts, the initial status of the banking system is analyzed as well as the methodologies of queue theory. The data was analyzed to modeling queuing systems for understanding queuing behavior using Wittness Software for simulation. Different queuing strategies will be implemented using the waiting time to find the most efficient solution and the optimized result is concluded.

Result: In this paper, the performance of the banking system is investigated and improved by the queuing theory. The sensitive analysis approach will provide new solutions for the optimization of the bank queuing, which could later be implemented for better banking performance. Based on the results obtained, the recommended method produces the best customer satisfaction and maximizes profits. The results of the paper enable decision-makers to obtain useful results with enough knowledge of the behavior of the system.

Limitation: This research only described the Iranian bank system. There is different limitation regarded as external factors that varies from one banking system to another and many works are needed to further combat the problems faced by the banking sectors.

Contribution: The results are a guideline for managers or decision makers and help them shorten cycle time and to save costs, and resolve problems. It also serves as a useful base for researchers to expand further research concerning the problems of the banking system in other organizations.

Keywords:
1. operation system
2. queue theory
3. optimization of the banking system
Authors:
Mohammad Forozandeh
University of Science and Technology (IUST), Tehran, Iran
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Forozandeh, M. (2022). Optimizing the Banking Service System Using Queue Theory, Fuzzy DEMATEL and TOPSIS Approach: Case Study. Annals of Human Resource Management Research, 2(2), 87–104. https://doi.org/10.35912/ahrmr.v2i2.1035
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Issue & Section
Vol. 2 No. 2 (2022): September
Articles
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

References

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    Allen, A. O. (1980). Queueing models of computer systems. Computer, 13(04), 13-24.

    Bagchi, S. (2015). Analyzing distributed remote process execution using queuing model. ????????, 16(1), 163-170.

    Birge, J. R., & Júdice, P. (2013). Long-term bank balance sheet management: Estimation and simulation of risk-factors. Journal of banking & finance, 37(12), 4711-4720.

    Csikósová, A., ?ulková, K., & Janošková, M. (2016). Evaluation of quantitative indicators of marketing activities in the banking sector. Journal of Business Research, 69(11), 5028-5033.

    Drehmann, M., & Gambacorta, L. (2012). The effects of countercyclical capital buffers on bank lending. Applied economics letters, 19(7), 603-608.

    Elsinger, H., Lehar, A., & Summer, M. (2006). Risk assessment for banking systems. Management science, 52(9), 1301-1314.

    Forozandeh, M. (2021). The effect of supply chain management challenges on research and development projects using Fuzzy DEMATEL and TOPSIS approach. Annals of Management and Organization Research, 2(3), 175-190.

    Forozandeh, M., Teimoury, E., & Makui, A. (2019). A mathematical formulation of time-cost and reliability optimization for supply chain management in research-development projects. Rairo-Operations Research, 53(4), 1385-1406.

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

    Hammond, D., & Mahesh, S. (1995). A simulation and analysis of bank teller manning. Paper presented at the Winter Simulation Conference Proceedings, 1995.

    Hao, T., & Yifei, T. (2011). Study on queuing system optimization of bank based on BPR. Procedia Environmental Sciences, 10, 640-646.

    Koetter, M., & Noth, F. (2013). IT use, productivity, and market power in banking. Journal of Financial Stability, 9(4), 695-704.

    Kosmidou, K., & Zopounidis, C. (2004). Combining goal programming model with simulation analysis for bank asset liability management. INFOR: Information Systems and Operational Research, 42(3), 175-187.

    Li, J. (2008). Queuing theory and the witness service used in supermarkets of the optimal design. Chinese Information technology management, 11(13), 75-78.

    Mahmood, K., Chilwan, A., Østerbø, O., & Jarschel, M. (2015). Modeling of OpenFlow?based software?defined networks: the multiple node case. IET Networks, 4(5), 278-284.

    Mappadang, A., Wijaya, A. M., & Mappadang, L. J. (2021). Financial performance, company size on the timeliness of financial reporting. Annals of Management and Organization Research, 2(4), 225-235.

    Martin, A., Lakshmi, T. M., & Venkatesan, V. P. (2014). An information delivery model for banking business. International Journal of Information Management, 34(2), 139-150.

    Moradi, A. M., & Beigi, N. A. K. (2020). Strategic management of organizational resources using predicting the organization's bankruptcy level: New approach using Monte Carlo simulation. Annals of Management and Organization Research, 2(2), 113-127.

    Moro, S., Cortez, P., & Rita, P. (2015). Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Systems with Applications, 42(3), 1314-1324.

    Nosek Jr, R. A., & Wilson, J. P. (2001). Queuing theory and customer satisfaction: a Review of terminology, trends, and applications to pharmacy practice. Hospital pharmacy, 36(3), 275-279.

    Olusola, M. S., Okolie, S., & Adesina, A. K. (2013). Queue management systems for congestion control: Case study of first bank, Nigeria. International Journal of Advanced Studies in Computers, Science and Engineering, 2(5), 54.

    Rasmussen, A., Yu, H., Ruepp, S., Berger, M. S., & Dittmann, L. (2014). Efficient round?robin multicast scheduling for input?queued switches. IET Networks, 3(4), 275-283.

    Sarkar, A., Mukhopadhyay, A. R., & Ghosh, S. K. (2011). Improvement of service quality by reducing waiting time for service. Simulation Modeling Practice and Theory, 19(7), 1689-1698.

    Sauer, C., MacNair, E., & Kurose, J. (1984). Queueing network simulations of computer communication. IEEE journal on selected areas in communications, 2(1), 203-220.

    Tan, Y., & Anchor, J. (2017). Does competition only impact on insolvency risk? New evidence from the Chinese banking industry. International Journal of Managerial Finance.

    Toloie-Eshlaghy, A., & Behbahaninezhad, S. (2020). Modeling and Optimization of Banking Processes for Human Resource Planning Utilizing Queuing Petri Nets. Paper presented at the 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS).

    Wang, X., & Jiao, Z. (2008). Research based on queuing theory of the problem of bank. Xiangtan Normal University, 30(1), 58-60.

    Xiao, H., & Zhang, G. (2010). The queuing theory application in bank service optimization. Paper presented at the 2010 International Conference on Logistics Systems and Intelligent Management (ICLSIM).

    Yang, C.-C. (2012). Service, investment, and risk management performance in commercial banks. The Service Industries Journal, 32(12), 2005-2025.

    Zhang, R. (2002). Analysis of the service sector queuing. Journal of Qiqihar University, 6, 41-43.

    Zhao, X. (2007). Queuing theory with the bank management innovation. Modern finance, 3, 9-10.

  1. Al-Mogren, A., Iftikhar, M., Imran, M., Xiong, N., & Guizani, S. (2015). Performance analysis of hybrid polling schemes with multiple classes of self-similar and long-range dependent traffic input. Journal of Internet Technology, 16(4), 615-628.
  2. Allen, A. O. (1980). Queueing models of computer systems. Computer, 13(04), 13-24.
  3. Bagchi, S. (2015). Analyzing distributed remote process execution using queuing model. ????????, 16(1), 163-170.
  4. Birge, J. R., & Júdice, P. (2013). Long-term bank balance sheet management: Estimation and simulation of risk-factors. Journal of banking & finance, 37(12), 4711-4720.
  5. Csikósová, A., ?ulková, K., & Janošková, M. (2016). Evaluation of quantitative indicators of marketing activities in the banking sector. Journal of Business Research, 69(11), 5028-5033.
  6. Drehmann, M., & Gambacorta, L. (2012). The effects of countercyclical capital buffers on bank lending. Applied economics letters, 19(7), 603-608.
  7. Elsinger, H., Lehar, A., & Summer, M. (2006). Risk assessment for banking systems. Management science, 52(9), 1301-1314.
  8. Forozandeh, M. (2021). The effect of supply chain management challenges on research and development projects using Fuzzy DEMATEL and TOPSIS approach. Annals of Management and Organization Research, 2(3), 175-190.
  9. Forozandeh, M., Teimoury, E., & Makui, A. (2019). A mathematical formulation of time-cost and reliability optimization for supply chain management in research-development projects. Rairo-Operations Research, 53(4), 1385-1406.
  10. Gross, D. (2008). Fundamentals of queueing theory: John Wiley & Sons.
  11. Hammond, D., & Mahesh, S. (1995). A simulation and analysis of bank teller manning. Paper presented at the Winter Simulation Conference Proceedings, 1995.
  12. Hao, T., & Yifei, T. (2011). Study on queuing system optimization of bank based on BPR. Procedia Environmental Sciences, 10, 640-646.
  13. Koetter, M., & Noth, F. (2013). IT use, productivity, and market power in banking. Journal of Financial Stability, 9(4), 695-704.
  14. Kosmidou, K., & Zopounidis, C. (2004). Combining goal programming model with simulation analysis for bank asset liability management. INFOR: Information Systems and Operational Research, 42(3), 175-187.
  15. Li, J. (2008). Queuing theory and the witness service used in supermarkets of the optimal design. Chinese Information technology management, 11(13), 75-78.
  16. Mahmood, K., Chilwan, A., Østerbø, O., & Jarschel, M. (2015). Modeling of OpenFlow?based software?defined networks: the multiple node case. IET Networks, 4(5), 278-284.
  17. Mappadang, A., Wijaya, A. M., & Mappadang, L. J. (2021). Financial performance, company size on the timeliness of financial reporting. Annals of Management and Organization Research, 2(4), 225-235.
  18. Martin, A., Lakshmi, T. M., & Venkatesan, V. P. (2014). An information delivery model for banking business. International Journal of Information Management, 34(2), 139-150.
  19. Moradi, A. M., & Beigi, N. A. K. (2020). Strategic management of organizational resources using predicting the organization's bankruptcy level: New approach using Monte Carlo simulation. Annals of Management and Organization Research, 2(2), 113-127.
  20. Moro, S., Cortez, P., & Rita, P. (2015). Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Systems with Applications, 42(3), 1314-1324.
  21. Nosek Jr, R. A., & Wilson, J. P. (2001). Queuing theory and customer satisfaction: a Review of terminology, trends, and applications to pharmacy practice. Hospital pharmacy, 36(3), 275-279.
  22. Olusola, M. S., Okolie, S., & Adesina, A. K. (2013). Queue management systems for congestion control: Case study of first bank, Nigeria. International Journal of Advanced Studies in Computers, Science and Engineering, 2(5), 54.
  23. Rasmussen, A., Yu, H., Ruepp, S., Berger, M. S., & Dittmann, L. (2014). Efficient round?robin multicast scheduling for input?queued switches. IET Networks, 3(4), 275-283.
  24. Sarkar, A., Mukhopadhyay, A. R., & Ghosh, S. K. (2011). Improvement of service quality by reducing waiting time for service. Simulation Modeling Practice and Theory, 19(7), 1689-1698.
  25. Sauer, C., MacNair, E., & Kurose, J. (1984). Queueing network simulations of computer communication. IEEE journal on selected areas in communications, 2(1), 203-220.
  26. Tan, Y., & Anchor, J. (2017). Does competition only impact on insolvency risk? New evidence from the Chinese banking industry. International Journal of Managerial Finance.
  27. Toloie-Eshlaghy, A., & Behbahaninezhad, S. (2020). Modeling and Optimization of Banking Processes for Human Resource Planning Utilizing Queuing Petri Nets. Paper presented at the 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS).
  28. Wang, X., & Jiao, Z. (2008). Research based on queuing theory of the problem of bank. Xiangtan Normal University, 30(1), 58-60.
  29. Xiao, H., & Zhang, G. (2010). The queuing theory application in bank service optimization. Paper presented at the 2010 International Conference on Logistics Systems and Intelligent Management (ICLSIM).
  30. Yang, C.-C. (2012). Service, investment, and risk management performance in commercial banks. The Service Industries Journal, 32(12), 2005-2025.
  31. Zhang, R. (2002). Analysis of the service sector queuing. Journal of Qiqihar University, 6, 41-43.
  32. Zhao, X. (2007). Queuing theory with the bank management innovation. Modern finance, 3, 9-10.