# Optimizing Aircraft Routes in Dynamic Conditions Utilizing Multi-Criteria Parameters
## 4 Results
### Practical Modeling  
This article focuses on modeling and searching for the optimal aircraft route using a real-world route as an example (with modeling of various constraints to verify the proposed solution). The following vehicles were selected for the computational modeling: the airliners Boeing 737 MAX and Airbus A320 neo (used for commercial flights). Aircraft parameters:  
1. Boeing 737 MAX:  
* Range: up to 6570 km;
* Cruise speed: 828 km/h;
* Advantages of the algorithm: efficiency, modern technologies, and reduced fuel consumption. 2. Airbus A320 neo:  
* Range: up to 6300 km;
* Cruise speed: 828 km/h;
* Advantages of the algorithm: fuel efficiency, the latest engines, and reduction of CO\({}_{2}\) emissions. Table 8 shows the main characteristics of the algorithms used in practical modeling based on aircraft characteristics. Table 9 shows the results of a comparative analysis of algorithms within the framework of accurate construction of aircraft logistics routes. Table 10 shows the results of a comparative analysis of the obtained practical modeling data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristic** & **Dijkstra** & **A*** & **B*** & **D*** \\ \hline \multirow{3}{*}{Graph type} & Undirected, & Undirected, & Undirected, & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } & Oriented, \\  & non-negative weights & non-negative weights & non-negative weights & with dynamic weights \\ \cline{1-1}  & No & Yes & No & Yes \\ \cline{1-1}  & O(V\({}^{2}\)) & & & \\ \cline{1-1} \cline{2-5} Time complexity & or & O(E logV) & O(E logV) & O(E logV) \\ \cline{1-1} \cline{2-5} Memory consumption & Large & Average & Average & Average \\ \cline{1-1} Flexibility & Low & High & High & High \\ \cline{1-1} Support for dynamic & & & & \\ \cline{1-1} changes & No & No & No & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Characteristics of algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Accuracy** & **Influence of Weather Conditions** \\ \cline{1-1}  & **(Excluding Weather)** & **(Weather-Adjusted ***)** & \\ \hline Dijkstra & High & Average & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } \\ A* & High & High & \\ B* & High & High & Depends on the quality of the heuristics \\ D* & High & High & \\ Advanced solution & High & High & \\ \hline \hline \end{tabular}  
* High—means that accuracy of generated routes is close (95% and higher) in comparison with the best route. Average—means that accuracy of generated routes is close (85–90%) in comparison with the best route. Weather-adjusted ***—means that weather impact was considered during route generation  
\end{table} 
Table 9: Results of the comparative analysis of algorithms. The results of the practical study (Table 10) according to the results of modeling show the following:  
1. [PERSON]'s algorithm is most suitable for graphs with non-negative weights but may be ineffective for dynamic conditions or changes in the graph. 2. When applying the A* algorithm, the use of heuristics can reduce the computation time. 3. The B* algorithm is similar to the A* algorithm but may be more effective in specific cases. 4. The D* algorithm copes well with dynamic changes in the graph, which makes it ideal for real-world conditions with constant changes. 5. The improved solution is suitable for specific cases and adapts to specific conditions, including responding to the impact of weather conditions. Table 11 shows the types of input data used to optimize the logistics routes of the selected aircraft. Table 12 shows the corrections when taking into account the impact of weather conditions on aircraft fuel consumption for the test modeling.