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.


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