# A Hybrid IVFF-AHP and Deep Reinforcement Learning Framework for an ATM Location and Routing Problem
## 1 Introduction
Since the ATM location decision has criteria that can be measured or expressed with subjective evaluations, it has also been the subject of studies on multi-criteria decision-making (MCDM) methods. [PERSON] et al. determined the quantitative and qualitative criteria to be considered in ATM site selection and obtained the importance levels of these criteria with the Analytic Network Process (ANP). According to the results of the study, while the most important criterion in the selection of the place is the salary payment point, it is observed that the average age of the population, the number of credit cards from other banks, the salary amount, and the number of employees in the company do not affect the selection [12]. [PERSON] and [PERSON] proposed a new model consisting of the fuzzy AHP and fuzzy ELECTRE III to find the optimal location for newly installed ATMs. Fuzzy AHP was used to find the criterion weights in the model, and the most important criterion was available ATM locations, including competing banks. A ranking of alternative ATM locations was obtained with ELECTRE III [13]. [PERSON] et al. (2018) [14] proposed, on the other hand, a new methodology consisting of ANP and goal programming to solve the site selection problem and support the methodology with a case study. In the proposed hybrid approach, ANP was used to obtain the weights of the criteria that were effective in site selection. With goal programming, they found the most suitable number of ATMs and their locations, minimizing the total number of ATMs and the distance between ATMs and customers and maximizing the total weighted score of the locations [14]. In their study, [PERSON] et al. used the Modified Delphi method to evaluate experts' criteria in ATM location problems. In the study, criterion weights were found by Pythagorean Fuzzy AHP, and the best alternative was found by Pythagorean Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The sensitivity analysis also discussed the study's results [15]. [PERSON] et al. determined the criteria and alternatives based on the customer data collected through the survey and chose the best ATM location using the AHP method. The study results showed the advantage of the AHP method in making quick decisions by considering customer requests among many alternatives in the ATM location problem [16]. When the studies in the literature are examined, a limited number of studies are found in which the ATM location problem is handled with GIS, data mining algorithms, heuristic algorithms, goal programming, and MCDM methods. In the existing studies, the problem of ATM location selection has been chiefly addressed regarding accessibility, customer satisfaction, and profitability. However, unlike the literature in this study, the safety risk is emphasized in the ATM location selection problem. Although the ATM location selection problem is generally addressed in terms of accessibility, satisfaction, and profitability in the existing literature, the operational efficiency of ATMs cannot be evaluated only by the correct location. For effective ATM management, cash-handling processes must also be optimized. Therefore, studies on routing between ATMs and CIT processes are becoming increasingly important. [PERSON] et al. (2017) [17] proposed an adaptive algorithm for optimizing CIT routes to minimize transport costs and safety risks. In the proposed model, the capacitated vehicle routing problem with time windows was considered to reduce the re-use of the route with high safety risk by considering the routes used in the past days. The model was tested with the data of a real CIT provider for different risk and cost scenarios, and it was observed that the model minimized the security risk and increased efficiency [17]. [PERSON] et al. applied a two-stage algorithm with economic and environmental objectives to the cash transport vehicle routing problem.