• Jul 03, 2017 News!Good News! Since 2017, IJMLC has been indexed by Scopus!
  • Aug 15, 2017 News![CFP] 2017 the annual meeting of IJMLC Editorial Board, ACMLC 2017, will be held in Singapore, December 8-10, 2017.   [Click]
  • Sep 09, 2017 News!Vol.7, No.4 has been published with online version.   [Click]
General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2011 Vol.1(1): 104-112 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.16

A Market-Based Study of Optimal ATM’S Deployment Strategy

Alaa Alhaffa and Wael Abdulal
Abstract—ATMs are critical to the success of any financial institution. Consumers continue to list the location of ATMs as one of their most important criteria in choosing a financial institution, for that banks are willing investment more ATMs for the purposes of providing greater convenience and attracting more customers. But there must be some equilibrium number of ATMs in the market otherwise rivals will enter the market and take all non-served customers. In the competitive case, the bank with most ATMs which are optimally deployed by using strong strategies would win the competition and get all the customers. Based on Bank clients’ base, this study has placed great emphasis on the ATM’s Deployment Strategies in order to provide greater convenience to the customers, consequently, banks can attract more customers and increase its market share and profitability. Technically, three algorithms are designed and compared namely; Heuristic Approach, Rank-Based Genetic Algorithm using Convolution and Simulated Annealing using Convolution. Dual objective is set to achieve highest Percentage Coverage (PC) and less ATMs Number required for covering intended area of study. Three experiments are carried out to measure the performance of each Algorithm. The experimental results show that Rank Based Genetic Algorithm shows a significant improvement in PC over Heuristic Approach, recording minimum improvement of 2.2% and maximum improvement of 20.13%. And it shows that Simulated Annealing outperforms both Heuristic Approach by up to 26.32% and Genetic Algorithm using convolution by up to 2.288% in terms of Percentage Coverage value. Regarding the saving in number of ATMs, Simulated Annealing Algorithm saves up to 33 ATMs over Heuristic Approach and up to 6 ATMs over Genetic Algorithm using Convolution.

Index Terms—Heuristic Approach using Convolution (HAC), Rank Based Genetic Algorithm using convolution (RGAC), Simulated Annealing using Convolution (SAC), Automated Teller Machines (ATMs).

Alaa Alhaffa, Dept. Economics, Osmania University, Hyderabad 500-007, India. (E-mail: alaa.haffa@yahoo.com). Wael Abdulal, Dept. CSE, EC, Osmania University, Hyderabad 500-007, India. (E-mail: wael.abdulal@ymail.com).


Cite:Alaa Alhaffa and Wael Abdulal, "A Market-Based Study of Optimal ATM’S Deployment Strategy," International Journal of Machine Learning and Computing vol. 1, no. 1, pp. 104-112, 2011.

Copyright © 2008-2015. International Journal of Machine Learning and Computing. All rights reserved.
E-mail: ijmlc@ejournal.net