# Optimizing Aircraft Routes in Dynamic Conditions Utilizing Multi-Criteria Parameters
## 3 Materials and Methods
The above are used in various algorithms to optimize the route searching process, taking into account various factors such as dynamic changes in conditions, forward motion, coordination of multiple agents, memory savings, and efficiency. \begin{table}
\begin{tabular}{c c c} \hline
**Research Area** & **Supplementation Needs** & **Explanation** \\ \hline \multirow{3}{*}{Integration of factors} & Development of universal models that simultaneously take into account weather conditions, fuel consumption, and other external factors to optimize aircraft routes. & Most existing models focus on individual factors, which limits their effectiveness in the complex conditions of real systems. \\ \hline \multirow{3}{*}{Variable conditions} & Creating models that adapt to changing external conditions in real time, including changes in weather and other important parameters. & Current solutions often cannot factor in dynamic changes in real time, which reduces their effectiveness in practical applications. \\ \hline \multirow{3}{*}{Economic efficiency} & Incorporating economic analysis into airline and logistics route planning models, which takes into account not only fuel consumption but also maintenance costs and other economic factors. & Existing approaches often focus on minimizing fuel consumption without considering other economic features. \\ \hline \multirow{3}{*}{Scalability of solutions} & Development of scalable algorithms that can work effectively in large aviation logistics systems with large amounts of data and numerous external factors. & Many of the existing models work effectively only on small systems, which limits their application in global aviation logistics. \\ \hline \multirow{3}{*}{Standardization of methods} & Introduction of standards and recommendations for building routes, factoring in external factors for emergency logistics. & The lack of standardized approaches makes it difficult to develop universal solutions and apply them in aviation logistics. \\ \hline \end{tabular}
\end{table} 
Table 2: Analysis of key areas for building optimal air routes for aircraft (which need to be supplemented as part of improving modern aviation logistics). The data shown in Table 4 clearly demonstrate how modern improvements to the A* algorithm make it possible to more effectively solve the problem of building optimal flight routes, taking into account dynamic changes in conditions, the coordination of multiple vessels, fuel, and computing resources. Each enhancement, such as D*, theta*, multi-agent A*, IDA*, and fringe search, has its own strengths, such as adaptation to changing conditions, improved performance, or memory savings, but each also has its limitations, such as implementation complexity and high computational load. Table 5 shows the results of the analysis of modern improvements to the B* algorithm. The table shows that modern improvements to the B* algorithm make it possible to solve the problem of finding optimal flight routes more efficiently, in particular, by reducing search resources, optimizing memory, and improving performance in large spaces. Each algorithm enhancement, such as B*, B_Lite*, and enhanced B*, offers specific benefits, such as search speed and resource savings, but each also has its limitations, such as possible trade-offs between computation speed and accuracy, or the need for additional optimization of the aircraft flight path.