Name
Abstract | ||
|---|---|---|
Banks require consistent expansion through its lifetime to be competitive and reliable to their customers. But no previous branch expansion model considers both existing customers and branches when solves branch location problems. We propose BigBank model for branch location problem based on clustering-Analytic Hierarchy Process (AHP)-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) where we consider both parties and their geographical positioning. After applying K-means clustering on uncovered customers, we take cluster centers as primary branch candidates and collect Geographic Information System (GIS) information about them. Then we use experts' opinions on branch location with four criteria and 12 different sub-criteria in the AHP method for ranking. Based on the ranking of the criteria of bank experts, our model computes the best possible location using the TOPSIS ranking method. We implement our model for a commercial bank in Bangladesh and show that our solution is always better in all three metrics considered in this literature from the traditional State of Art solution even in different fiscal years. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SITIS.2019.00058 | 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) |
Keywords | DocType | ISBN |
AHP, TOPSIS, K means clustering, Bank Branch, GIS | Conference | 978-1-7281-5687-3 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sadia Sharmin | 1 | 5 | 6.13 |
| Kh. Solaiman | 2 | 0 | 0.34 |