Multi-Objective Problem of Services Assignment to Bank Clustered Customers

Document Type : Original Article


1 Associate Professor, Allameh Tabataba’i University.

2 Ph.D student, Allameh Tabataba’i University.

3 Assistant Professor, Allameh Tabataba’i University.


Knowing customer behavior patterns clustering and assigning them is one of the most important purposes for banks. In this research five criteria of each customer including Recency Frequency Monetary Loan and Deferred were extracted from the bank database during one year and then clustered using the customer's K-Means algorithm. A multi-objective model of bank service allocation was then designed for each of the clusters. The purpose of the designed model was to increase customer satisfaction reduce costs and reduce risk of allocating services. Given the fact that the problem does a unique optimal solution and each client feature has a probability distribution function a simulation approach was used to solve it. In order to determine the neighbor optimal solution of the Simulated Annealing algorithm neighboring solutions were used and a simulation model was implemented. The results showed a significant improvement over the current situation. In this research we used Weka and R-Studio software for data mining and Arena for simulation for optimization.


1. Abiodun, R. (2017). Development of Mathematical Models for Predicting Customers Satisfaction in the Banking System with a Queuing Model Using Regression Method. American Journal of Operations Management and Information Systems, 2(2), 86-91.
2. Adeli, M., Zandieh, M. (2013). Provide multi-objective simulation optimization approach for source modeling and integrated inventory decisions. Industrial Management Perspective11, 89-110 (In Persian).
3. Akbariasl, R., & Bashli, M. (2014) Banking Services Marketing, 81-92, Ettehad Publishing, Tehran, Iran (In Persian).
4. Bahmand, M., & Bahmani, M. (2006) Internal Banking (Supply of Money Resources), Iranian Institute of Banking Publisher, Tehran, Iran, 48-50 (In Persian).
5. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0: Step-by-step data mining guide. SPSS Incc.
6. Chen, Q., Zhang, M., & Zhao, X. (2017). Analysing customer behaviour in mobile app usage. Industrial Management & Data Systems, 117, 425-438.
7. Ghorbanpour, A., tallai, G., panahi, M. (2015). Clustering Customers of Refah Bank Branches Using Combination of Genetic Algorithm and C- Means in Fuzzy Environment. Organizational Resources Management Researchs, 5(3), 153-168 (In Persian).
8. Kalantari, M., Pishvaei, M., & Yaghoubi, S. (2015). A multi-objective optimization model for the integration of financial and physical flows in the mainstream supply chain planning. . Industrial Management Perspective, 19, 139-167 (In Persian).
9. Hartigan, J. (1975). Clustering algorithms. Wiley New York.
10. Hughes, A. M. (1996). Boosting reponse with RFM. Mark. Tools 5 4-10.
11. Peker, S., Kocyigit, A., & Erhan, E. (2017). LRFMP model for customer segmentation in the grocery retail industry: a case study. Marketing Intelligence & Planning, 35, 544-559.
12. Momeni, M. (2012). Data Clustering (Cluster Analysis), Danesh Negar Publisher, Tehran, Iran, 37-38 (In Persian).
13. Reinartz, W. J., & Kumar, V. (2003). The impact of customer relationship characteristics on profitable lifetime duration. Journal of Marketing, 67(1), 77-99.
14. Sajjadi, K., Khatami-Firuzabadi, M. A., Amiri, M., & Sadaghiani, J. S. (2015).“A developing model for clustering and ranking bank customers. International Journal of Electronic Customer Relationship Management”, 9(1), 73-86.
15. Singh, S., & Singh, S. (2016). Accounting for risk in the traditional RFM approach. Management Research Review, 39(2), 215-234.
16. Shahbandarzadeh, H., & Pikam, A. (2015). Application of a Fuzzy Factor Multi-Objective Model for Determining the Optimal Purchasing Volume of Suppliers. Industrial Management Perspective18, 129-152 (In Persian).
17. Taghavifard, M., Khajvand, S. & Najafi, E. (2013). ‘Customer clustering Saderat Bank of Iran by using data mining’. Improvement Management Studies, 67(21), 197-200 (In Persian).
18. Thomas, J. S. (2001). A methodology for linking customer acquisition to customer retention. Journal of Marketing Research 38(2), 262-268.
19. Ward, j. h. jr. (1963). hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236-244.
20. Wu Hsin-Hung; Chang En-Chi & Lo Chiao-Fang (2009). Applying RFM model and K-means method in customer value analysis of an outfitter. International Conference on Concurrent Engineering New York.
21. Zabkowski, T. (2016). RFM approach for telecom insolvency modeling. Kybernetes, 45(5), 815-827.