# A Hybrid IVFF-AHP and Deep Reinforcement Learning Framework for an ATM Location and Routing Problem
## 5 Discussion and Implications  
As in all sectors, it is possible to see the impact of information technologies in banking and finance. In the banking sector, informatics is important in determining the business structure. The information network, which enables the products to reach the buyer easily after the marketing stage, positively affects the banking sector if used correctly and efficiently. For banks operating in the field of banking to become more robust in terms of competition, they should closely follow the technological innovations of the sector and adapt them to themselves. The development of the technological infrastructure and the banking sector's automation system provide advantages in many ways. ATMs are the most prominent form of retail banking equipment. They are critical in any retail bank's efforts to employ technology as a quality weapon against competitors. This facility is important in providing convenient, quick, and round-the-clock services [42]. ATMs can perform balance and transaction inquiries, withdrawals, deposits, and account transfers. As a result of the MCDM analysis, \"demographic\" is determined to be the most important main criterion. ATM locations should be placed in an area with a high population density based on the customer profile. If ATMs are placed in inappropriate areas, customers cannot use them optimally. In this context, customer profile data can provide information about ATM usage at the relevant location. Traffic density is determined as the most important sub-criteria among twenty sub-criteria. Therefore, decision-makers should \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**DDQN**} & \multicolumn{2}{c}{**Gurobi**} \\ \hline  & **Scenario** & **Visited Nodes** & **Total Distance** & **Total Distance** \\ \cline{2-5}
1 & Initial ATMs & [911, 882, 239, 110, 127, 482, 175, 228] & 9.38 & 16.31 \\
2 & One ATM (911) is deleted or & [201, 110, 127, 228] & 5.46 & 5.97 \\  & & two ATMs (911, 127) are & [201, 110, 3148, 3152, 228] & 5.64 & 12.33 \\
3 & deleted or out of order & [201, 110, 3148, 3152, 228] & 13.20 & 22.82 \\
4 & & the initial ATMs & [201, 110, 3148, 3165, 213, 228] & 19.48 & 20.81 \\  & & initial ATMs & [201, 110, 3148, 3165, 213, 228] & 19.48 & 20.81 \\  & & initial ATMs & [201, 110, 3148, 3165, 213, 228] & 191.18 & \\
6 & & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of DDQN and Gurobi heuristics. choose easily accessible locations with low traffic density for ATMs. Especially in a city with high traffic density such as Istanbul, people need to access ATMs quickly and efficiently to perform their transactions. However, visibility is also a vital sub-criterion in ATM location selection. Although the population density is high and the traffic is low, the relevant ATM cannot be used effectively enough if the visibility is low. Traffic density plays a major role not only in ATM location selection but also in the efficient, safe, and dynamic management of cash transportation processes. Furthermore, as supported by the literature, high traffic density increases exposure time on the road, thereby elevating the risk of theft. In response, real-time traffic data were collected and normalized to construct a risk matrix. This matrix was then integrated into the distance matrix to simultaneously account for both travel cost and security risk. As a result, the proposed methodology offers a comprehensive solution that goes beyond classical distance-based optimization by incorporating real-world security concerns into the decision-making process. In this direction, dynamic-based route planning was made using real traffic data with the DDQN-RL-based vehicle routing model, and security and efficiency increases were achieved in CIT operations. The results of the study provide significant contributions in academic and sectoral terms.