A Market-Based Study of Optimal Atm’s Deployment Strategy

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A market-Based study of optimal ATM’s Deployment Strategy
Alaa Alhaffa Wael Abdulal Dept. Economics Dept. CSE, EC Osmania University Osmania University Hyderabad 500-007, India Hyderabad 500-007, India E-mail: alaa.haffa@yahoo.com E-mail: wael.abdulal@ymail.com 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).

In order to survive, both banks and ATM deployers need to anticipate new customer needs, respond much more rapidly to competitive changes and create new sources of customer value and service differentiation. The ATM optimal Deployment Strategies offer the opportunity to provide greater convenience and to attract more customers by covering the money market with sufficient ATM facilities. These strategies also provide greater cost efficiency by finding the optimal number of ATMs to be installed and greater profitability by increasing the ATM user base in order to earn much more transactions and services fees [1] as well as through the inflow of deposits from the depositors who consider ATM availability as a main factor in choosing their banks. ATMs have become a competitive weapon to commercial banks whose objective is to capture the maximum potential customers. One important fact to be noted is that commercial banks compete not only on the dimension of price but also on the dimension of location [2]. The problem of ATM deployment is seen to be NP-complete problem (it is analogous to the file server placement problem) [3]. In order to solve this problem, three algorithms are designed and compared namely;...
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