Abstract
This work is concerned with the fuzzy clustering problem of different products in j variant catalogs, each of size i products that maximize customer satisfaction level in customer relationship management. The satisfaction degree of each customer is defined as a function of his/her needed product number that exists in catalog and also his/her priority. To determine the priority level of each customer, firstly customers are divided to three clusters with high, medium and low importance based on his/her needed products list. Then, all customers have been ranked based on their membership level in each of the above three clusters. In this paper in order to cluster customers, fuzzy c-means algorithm is applied. The proposed problem is firstly modeled as a bi-objective mathematical programming model. The objective functions of the model are to maximize the number of covered customers and overall satisfaction level results of delivering service. Then, this model is changed to a single integer linear programming model by applying fuzzy theory concepts. Finally, the efficiency of the proposed solution procedure is verified by using a numerical example.
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Zadeh, A.H., Maleki, H., Kianfar, K., Fathi, M., Zaeri, M.S. (2010). Catalog Segmentation by Implementing Fuzzy Clustering and Mathematical Programming Model. In: Corchado, E., Novais, P., Analide, C., Sedano, J. (eds) Soft Computing Models in Industrial and Environmental Applications, 5th International Workshop (SOCO 2010). Advances in Intelligent and Soft Computing, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13161-5_21
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DOI: https://doi.org/10.1007/978-3-642-13161-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13160-8
Online ISBN: 978-3-642-13161-5
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