# A Hybrid IVFF-AHP and Deep Reinforcement Learning Framework for an ATM Location and Routing Problem
## 6 Conclusions and Further Research  
As a result, this study proposes a hybrid approach to evaluate ATM locations and plan vehicle routing between these locations to provide the most reliable transportation operations for the CIT process. One of the research topics of this study is the ATM location selection problem and which criteria should be considered in ATM location selection. Another topic is the route optimization of cash transportation vehicles. A real-life study is conducted in Istanbul, the most populous city of Turkey, to demonstrate the procedure for solving ATM location selection and vehicle routing problems. Therefore, the problem is initially modeled as a decision-making problem and the criteria are established based on literature review and expert opinions. Clustering approaches determine the high-risk district and then determine the most suitable location according to the weights obtained by the IVFF-AHP method. The most suitable location is determined by the clustering approaches according to the weights determined by the IVFF-AHP method. According to the clustering approach, the riskiest district of Istanbul is selected as Esenyurt, and the most suitable ATM location in this district is determined. Of the four main criteria for location selection, the most important one is \"demographic\", and the least important one is \"commercial and financial\". In the decision-making research for Istanbul, Esenyurt Hospital was determined to be the most suitable location, with a final score of 0.27. This alternative is followed by Hotel Sheraton Esenyurt, with a final score of 0.26. When the main and sub-criteria are considered and more than one expert evaluates the final findings, the Esenyurt Hospital location, which ranks first in terms of low traffic density, high population density, and security, seems quite reasonable. Besides optimizing ATM placement, the proposed methodology also enables CIT vehicle routing using DDQN-RL. A dynamic routing system is developed by integrating real traffic data into the route optimization of the located ATMs to improve operation efficiency. Unlike traditional vehicle routing methods, the proposed DDQN-RL model enables the CIT vehicle to adapt to demand changes, traffic fluctuations, and ATM network changes, thus providing service with minimum cost and delay. This study contributes to the literature on location selection decision-making problems. The contributions of this study to the literature are as follows: (i) The most detailed criteria structure was developed to analyze alternative ATM locations, considering the risks associated with CIT operations; (ii) In this study, the IVFF-AHP method was used for the first time in the ATM location selection problem; (iii) For the first time in the literature, a clustering approach was integrated into the IVFF-AHP methodology to limit and prioritize the district to be selected for ATM locations; (iv) The location selection was furthered by applying the DDQN-RL model using real traffic data to make the cash transportation procedure more efficient; (v) In order to demonstrate the applicability of the proposed hybrid methodology, four alternatives were tested in Istanbul, and the optimum route was selected for the alternatives. Future research can investigate the integration of alternative MCDM approaches, such as TOPSIS and VIKOR, into the IVFF set. In addition, the solution model can be divided into location selection and cash management. These decisions are interdependent because cash management strategies should be established according to the priority order depending on the ATM locations. Static traffic data can be fed into the DDQN-RL model for vehicle routing, and, also, the routing problem of multiple CIT vehicles can be optimized.