# A Hybrid IVFF-AHP and Deep Reinforcement Learning Framework for an ATM Location and Routing Problem
## 4 A Real Case Study
### ATM Routing Network  
In this part of the study, a routing is planned for ATMs in the selected region. Reinforcement Learning (RL) is used in this study to determine the optimal route between ATM locations. This method has several advantages, including recognizing the best route based on real-time traffic conditions, adapting to changing road and traffic situations, and self-improvement through trial and error. In contrast to the conventional paradigms, RL can interpret real-time traffic patterns and process long-term route planning. This is emerging as an adaptive and intelligent solution. Therefore, a dynamic routing system is established to improve ATM operational efficiency in terms of time and fuel savings. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Rank**} \\ \hline  & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Scenario 4** \\ \hline A-2: Esenyurt Hospital & 2 & 2 & 2 & 2 \\ A-1: Hotel Sheraton & 1 & 1 & 1 & 1 \\ Esenyurt & 3 & 3 & 3 & 3 \\ A-4: Torium Mall & 4 & 4 & 4 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The final ranks of alternatives. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Weights**} \\ \hline  & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Scenario 4** \\ \hline A-2: Esenyurt Hospital & 1.2572 & 1.2947 & 1.2616 & 1.2465 \\ A-1: Hotel Sheraton & 1.3579 & 1.4037 & 1.3578 & 1.3106 \\ Esenyurt & 1.255 & 1.278 & 1.2039 & 1.2131 \\ A-4: Torium Mall & 1.1761 & 1.1721 & 1.1448 & 1.097 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The final weights of alternatives. Figure 7 is created using Folium and NetworkX libraries and is designed to route between four key ATM points in the Esenyurt region. Folium is a powerful library that allows the creation of interactive maps using Python 3.10.14 It is widely used to visualize geographic data, make visualizations on maps, and make analyses interactive. It helps to make sense of data by adding markers, lines, and various layers to maps. Maps saved in HTML format can be interactively viewed and analyzed via web browsers. In this study, for ease of display and operation, the 30 most central points were selected instead of all intersections, thus creating a network of hubs that provide connectivity for all roads. Four of these centers are where ATM points are located; the node numbers of Hotel Sheraton Esenyurt, Esenyurt Hospital, Esenyurt City Hall, and Torium Mall are 911, 110, 127, and 228, respectively. On the map, the road structure of the entire network is shown with blue lines so that the existing connections are visible. The red circles represent the nodes to be used for possible routing. By determining the most suitable paths between these nodes, routing between ATM points can be realized. The ATM vehicle (agent) was trained using the DDQN-RL algorithm, enabling it to learn the optimal route based on traffic density variations. Unlike conventional Q-learning methods, DDQN-RL provides more stable and accurate decision-making capabilities as a deep reinforcement learning approach. This model continuously determines the shortest and most efficient route based on traffic conditions, dynamically updates the distance matrix to generate optimized paths for each time interval, and enables the ATM vehicle to learn the most effective route by making decisions according to temporal and traffic variations. Such an approach is particularly critical in cash-in-transit (CIT) operations, where high traffic density has been shown to correlate with increased vulnerability to robbery attempts. Therefore, directing CIT vehicles through lower traffic density regions not only enhances operational efficiency but also significantly reduces the likelihood of ambushes and security breaches. This finding is consistent with recent studies emphasizing the integration of real-time traffic data into route planning to mitigate robbery risks [39; 40; 41]. The result is that this study develops an optimized routing system for ATM vehicles by leveraging traffic data.