# Optimizing Aircraft Routes in Dynamic Conditions Utilizing Multi-Criteria Parameters
## 4 Results
### Practical Modeling
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & & & \\ \hline \multirow{2}{*}{**Route**} & \multirow{2}{*}{**Time of Year**} & \multirow{2}{*}{**Dijkstra: Duration, Hours**} & **A***: Duration, Hours** & **B***: Duration, Hours** & **D***: Duration, Hours** & **Advanced Solution: Duration, Hours** & **Influence of Weather Conditions** \\ \hline London– New York & Summer & 7.5 & 7.3 & 7.2 & 7.1 & 7.0 & Average \\ London– New York & Winter & 8.0 & 7.7 & 7.6 & 7.5 & 7.4 & High \\ \hline \hline \multirow{2}{*}{**Route**} & \multirow{2}{*}{**Time of Year**} & \multirow{2}{*}{**Dijkstra: Duration, Hours**} & **A***: Duration, Hours** & **B***: Duration, Hours** & **D***: Duration, Hours** & **Advanced Solution: Duration, Hours** & **Influence of Weather Conditions** \\ \hline London– New York & Summer & 7.0 & 6.8 & 6.7 & 6.6 & 6.5 & Average \\ London– New York & Winter & 7.5 & 7.3 & 7.2 & 7.1 & 7.0 & High \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparative logistics modeling of aircraft routes (results). The results of the analysis show that the improved solution provides significant advantages over the classical algorithms (Dijkstra, A*, B*, and D*). These advantages are expressed in a reduction in flight time and fuel consumption, which is especially noticeable in weather changes, such as winter conditions, where all algorithms show a decrease in efficiency, but the improved solution shows the least impact. In the summer, when warmer weather can change the air density and require more hard work from the engines (the density altitude effect), the advanced solution also continues to outperform other methods, delivering savings in both flight time and fuel consumption. This demonstrates its ability to effectively handle route optimization in stable weather conditions. The advantages of the improved solution are mainly related to its adaptability to dynamic changes and reduced sensitivity to changes in weather conditions, which increases its efficiency compared to less flexible algorithms. It is worth noting that the implementation and adaptation of the improved solution may require additional resources for customization and testing, which is a limitation for its widespread use. However, the benefits in terms of cost reduction and improved route execution time compensate for these costs, making it a promising choice for optimizing airline routes in real-world conditions. The analysis of the comparative modeling results shows that the improved solution demonstrates significant advantages in optimizing aircraft routes compared to classical algorithms. For example, for the London-New York route of Airbus A320 neo aircraft, the improved solution reduced the flight duration in summer to 7.0 h, which is a better result compared to 7.5 h when using the Dijkstra algorithm. In winter, the advanced solution also outperforms all other methods, providing a flight duration of 7.4 h versus 8.0 h when using Dijkstra. The improved solution shows significant benefits in terms of fuel consumption (Table 13). For the Airbus A320 neo, in summer, the fuel consumption is 2450 L/h, which is the lowest value among all the algorithms considered, compared to 2500 L/h using the Dijkstra algorithm. In winter, the difference is even more pronounced: the improved solution demonstrates a consumption of 2550 L/h, which is better than the 2600 L/h recorded using Dijkstra. For the Boeing 737 MAX, the improved solution also demonstrates the benefits of the algorithm: in summer, fuel consumption was reduced to 2750 L/h, which is better than the 2800 L/h recorded with Dijkstra. In winter, the improved solution provides a fuel consumption of 2850 L/h, which is the lowest value compared to 2900 L/h for the Dijkstra algorithm. Our enhanced solution takes into account weather conditions more effectively, which is reflected in reduced fuel consumption and flight time in winter, when the impact of weather conditions is high, showing that our solution adapts better to dynamic conditions compared to existing approaches.