## 6 Conclusions  
In this paper, the operation scheduling problem of apron support vehicles was studied, and a new method for the improvement of the airport's support capability and the efficiency of aircraft re-deployment was proposed. By using the empirical method, a multi-machine, multi-guaranteed vehicle, and multi-operation process apron support vehicle scheduling model was established, and a multi-layer coding genetic algorithm was designed to solve the operation scheduling problem of the apron support vehicle. Combined with the actual aircraft support of an airport, the accuracy of the model was verified. The algorithm converges after 50 iterations, thereby confirming the feasibility of the algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Aircraft** &
\begin{tabular}{c} **Vehicle Configuration** \\ **(Re:Ox:Po)** \\ \end{tabular} & **Vehicle Scheduling** & **Least Time/Min** \\ \hline \hline \multirow{4}{*}{1} & \multirow{4}{*}{1:1:1} & Re: A1 & \multirow{4}{*}{55} \\  & & Oc: A1 & & \\  & & P: A1 & & \\ \hline \hline \multirow{4}{*}{2} & \multirow{4}{*}{1:1:1} & Re: A1 \(\rightarrow\) A2 & \multirow{4}{*}{60} \\  & & Oc: A2 \(\rightarrow\) A1 & & \\ \cline{1-1}  & & Oc: A1 & & \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{2:1:1} & Re: A2 & \multirow{4}{*}{60} \\  & & Re2: A3 \(\rightarrow\) A1 & & \\ \cline{1-1}  & & Oc: A1 \(\rightarrow\) A3 \(\rightarrow\) A2 & & \\ \cline{1-1}  & & Oc: A1 \(\rightarrow\) A2 \(\rightarrow\) A3 & & \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{2:1:1} & Re: A3 \(\rightarrow\) A2 & \multirow{4}{*}{60} \\  & & Re2: A4 \(\rightarrow\) A1 & & \\ \cline{1-1}  & & Oc: A2 \(\rightarrow\) A1 \(\rightarrow\) A3 & & \\ \cline{1-1}  & & Oc: A2 \(\rightarrow\) A4 & & \\ \cline{1-1}  & & Oc: A3 & & \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{3:2:1} & Re1: A5 & \multirow{4}{*}{65} \\  & & Re2: A2 \(\rightarrow\) A1 & & \\ \cline{1-1}  & & Re3: A4 \(\rightarrow\) A3 & & \\ \cline{1-1}  & & Oc: A2 \(\rightarrow\) A1 \(\rightarrow\) A3 & & \\ \cline{1-1}  & & Oc: A1 \(\rightarrow\) A3 \(\rightarrow\) A2 & & \\ \cline{1-1}  & & Oc: A5 \(\rightarrow\) A4 & & \\ \hline \multirow{4}{*}{6} & \multirow{4}{*}{3:2:2} & Re1: A5 \(\rightarrow\) A2 & \multirow{4}{*}{60} \\  & & Re2: A4 \(\rightarrow\) A3 & & \\ \cline{1-1}  & & Re3: A6 \(\rightarrow\) A1 & & \\ \cline{1-1}  & & Oc: A2 \(\rightarrow\) A4 \(\rightarrow\) A6 & & \\ \cline{1-1}  & & Oc: A3 \(\rightarrow\) A1 \(\rightarrow\) A5 & & \\ \cline{1-1}  & & Oc: A1 \(\rightarrow\) A3 \(\rightarrow\) A5 & & \\ \cline{1-1}  & & Oc: A2 \(\rightarrow\) A6 \(\rightarrow\) A4 & & \\ \hline \hline \end{tabular}  
\(\mathrm{\AA}=\) aircraft.  
\end{table} 
Table 9: Optimization scheme of airport apron support vehicles. Compared with the traditional support method, the proposed method can be used to greatly improve the utilization rate of the support vehicle while reducing the equipment support time. Among them, the time to protect 4 aircraft and 6 aircraft was significantly shortened by 40 min, while the time to protect 2 aircraft, 3 aircraft, and 5 aircraft was shortened by 25 min. In addition, with the increase of the number of support aircraft, the utilization rate of the oxygenation refilling truck and power van has been greatly improved. Although the utilization rate of the refueling truck has decreased, the utilization rate is still above 70%. Considering that the number of support vehicles limits the further shortening of the support time and the phenomenon that the utilization rate of power van is still low after optimization, the number of support vehicles was optimized twice. Within 65 min, the average utilization rates of refueling vehicles, oxygen-charging vehicles, and power van are 100%, 83%, and 65%, respectively, reaching a high level, which further saves time and improves resource utilization. At present, there are still gaps between the research model and the actual situation.