# A Hybrid IVFF-AHP and Deep Reinforcement Learning Framework for an ATM Location and Routing Problem
## 1 Introduction  
Today, minimizing the total cost and maximizing customer satisfaction have become the primary goals for companies in the service industry. Delivering the service to the customer when the customer needs it is the most decisive criterion of customer satisfaction. Especially in the banking sector, where continuous development and change are experienced, institutions must improve their competitive conditions in terms of performance evaluations [1]. The fact that banks focus on technological investments and offer innovative products and services further increases the competition in the sector. For this purpose, banks are investing more in mobile applications and the Internet. ATMs are one of the most prominent examples of these investments. ATMs play a significant role for banks as they contribute to sustainability in the banking industry and reduce transaction costs. ATMs are the first alternative node of service delivery that enables faster delivery of banking services. ATMs have many advantages,such as serving customers in a broader geography, reducing the density of bank branches, and additional commission income. Due to the increasing speed of digitalization and globalization, the number of ATMs is increasing daily [2]. On the other hand, ATM management is a complex process with many variables and outsourced companies such as customers, headquarters, branches, and CIT operations. However, cash management consists of many sub-processes, such as distribution, security, and maintenance. The location of the ATM plays a significant role in the distribution process, which is included in these processes. The location of ATMs is vital because it affects profitability, customer satisfaction, and possible losses [3]. If banks install ATMs in inappropriate places, problems such as the machines not being used optimally by customers and an increase in operating costs arise. Accordingly, ATMs have to be moved or reactivated due to wrong decisions. As customers can perceive this situation, it may result in a loss of reputation for the bank. Several studies use different methods to determine the best location for ATMs. [PERSON] et al. developed a model combining the Geographic Information System (GIS) and a mathematical model. In the model, they make predictions by investigating the factors in the location planning of ATMs, thanks to the particle swarm optimization algorithm [4]. On the other hand, [PERSON] and [PERSON] discuss the location selection problem hierarchically, such as ATMs, bank branch offices, and central banks, and propose a stochastic model for solving the problem. The proposed model can work with different dynamically changing scenarios and can balance conflicting objectives such as profit and risk depending on the bank's strategies according to the changing situations. The model also considers the uncertainty and stochasticity caused by the variability of criteria such as taxes, interest rates, and inflation [5], and in their study using GIS, [PERSON] and [PERSON] developed an area density and linear population carrying capacity model based on the people, economic situation, and existing ATM network in the district they examined. In this way, ATM distribution imbalances could be eliminated, and the coverage area could be maximized [6]. In another GIS study, potential ATM locations were determined by considering existing ATM locations, customer density, road network, and other facilities [7]. In another study, [PERSON] et al. addressed the ATM location problem with regression analysis, one of the statistical methods. After determining the criteria affecting site selection by correlation analysis, optimum ATM locations were found with least squares regression analysis [8]. Several studies have utilized decision trees, clustering algorithms, and statistical models to determine optimal ATM locations based on criteria such as population density, competition, or rent costs [9; 10; 11].