Evaluating the Bank Queuing Systems by Fuzzy Logic

Abstract

Various models are used in the banking system to organize the queue structure of customers' banking transactions. The average waiting time for a customer in the queue generally varies depending on whether bank customer or not and the customer score it has. Different uncertain parameters are used to determine the individual queue group and average waiting time in bank queuing systems. This paper proposes a fuzzy logic-based approach in bank queuing systems. In this study, individual bank queue group and average waiting times are determined according to the number of waiting customers, customer score and credit score parameters. In addition, identification number is a determining factor for the priority of transactions in bank queuing systems. People who are not customers of the bank often have longer waiting times. As a new approach to the working structure of bank queuing systems, this study also suggests that non-bank customers should be given priority sequence numbers according to their credit scores.

Keywords

• Bank queue evaluation
• Fuzzy Logic
• Fuzzy Model
• Q-Matic

References

1. M. Mutingi, H. Mapfaira, N. P. K. Moakofi, S. A. Moeng, and C. Mbohwa, “Simulation and analysis of a bank queuing system,” in 2015 International Conference on Industrial Engineering and Operations Management (IEOM), 2015, pp. 1–6, doi: 10.1109/IEOM.2015.7093836.
2. N. Madadi, A. H. Roudsari, K. Y. Wong, and M. R. Galankashi, “Modeling and Simulation of a Bank Queuing System,” in 2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation, 2013, pp. 209–215, doi: 10.1109/CIMSim.2013.41.
3. H. R. Bakari, H. A. Chamalwa, and A. M. Baba, “Queuing process and its application to customer service delivery (A case study of Fidelity Bank Plc, Maiduguri),” Int. J. Math. Stat. Invent., vol. 2, no. 1, pp. 14–21, 2014.
4. A. Ullah, K. Iqbal, X. Zhang, and M. Ayat, “Sub-optimization of bank queuing system by qualitative and quantitative analysis,” in 2014 11th International Conference on Service Systems and Service Management (ICSSSM), 2014, pp. 1–6, doi: 10.1109/ICSSSM.2014.6874038.
5. A. S. A. Al-Jumaily and H. K. T. Al-Jobori, “Automatic queuing model for banking applications,” IJACSA) Int. J. Adv. Comput. Sci. Appl., vol. 2, no. 7, 2011.
6. S. Wang and N. U. Ahmed, “Dynamic Model of Bank Queuing System and Its Optimal Management,” in 2018 IEEE 4th International Conference on Control Science and Systems Engineering (ICCSSE), 2018, pp. 510–514, doi: 10.1109/CCSSE.2018.8724754.
7. H. Xiao and G. Zhang, “The queuing theory application in bank service optimization,” in 2010 International Conference on Logistics Systems and Intelligent Management (ICLSIM), 2010, vol. 2, pp. 1097–1100, doi: 10.1109/ICLSIM.2010.5461127.
8. M. Sarı, Y. Sazi Murat, and M. Kırabalı, “Bulanık Modelleme Yaklaşımı ve Uygulamaları,” Dumlupınar Üniversitesi Fen Bilim. Enstitüsü Derg., no. 009, pp. 77–92, 2005.

9. T. Aslan, E. Yılmaz, “Bulanık Mantık Yöntemi İle Belirsizlik Şartlarında Faaliyet-Hacim-Kar Analizi,” İşletme Araştırmaları Dergisi, 10/2, 534-553,2018.
10. A. E. Tiryaki and R. Kazan, “Bulaşık makinesinin bulanık mantık ile modellenmesi,” Mühendis ve Makine, vol. 48, no. 565, pp. 3–8, 2007.
11. Ö. Kişi, M. E. Karahan, and Z. Şen, “Nehirlerdeki askı maddesi miktarının bulanık mantık ile modellenmesi,” İTÜ Dergisi/d Mühendislik, vol. 2, no. 3, pp. 43–54, 2003.
12. Ö. Ahmet and M. Sinecen, “Klima Sistem Kontrolünün Bulanık Mantık ile Modellemesi,” Pamukkale Üniversitesi Mühendislik Bilim. Derg., vol. 10, no. 3, pp. 353–358, 2004.
13. Y. Ş. Murat, “Sinyalize kavşaklardaki taşıt gecikmelerinin bulanık mantık ile modellenmesi,” İMO Tek. Dergi, vol. 3903, no. 3916, p. 258, 2006.