## 5 Examples of Scheduling Optimization of Apron Support Vehicles
### Simulation 2: Configuration Optimization of Support Vehicle Quantity
It can be seen from Figure 7 that with the increase of the number of support aircraft, the utilization rates of the refueling truck before the optimization are all 100%, which decrease after the optimization, but the average utilization rate reaches more than 70%. On the other hand, Figure 8 shows that with the increase of the number of support aircraft, the average utilization rate of the oxygenation refilling truck before optimization gradually increases, which is basically between 30% and 80%. While the utilization rate of the oxygenation refilling truck after optimization is 0-39% higher than that before optimization, and the average is between 33% and 100%. As seen from Figure 9, with the increase of the number of support aircraft, the fluctuation of the power van before optimization becomes small, which basically ranges from 18% to 26%. The utilization rate of the optimized power van increases gradually, which is higher than that before optimization by 0-59%, i.e., between 18% and 80%. From the vehicle scheduling optimization results of multi-aircraft support, it can be found that the total time of 3-aircraft support reaches 90 min, exceeding the support time of 4 aircraft; a 4-aircraft power van worked for 10 min; two of the 5-aircraft power van worked for 10 min, and the utilization rate of the refueling trucks decreased compared with that before optimization, and the total time reached 90 min. Two and three of the six-aircraft power van worked for 10 min, the utilization rate of the refueling vehicle decreased, and the power van stood in the way of shortening the time. Therefore, the vehicle configuration is adjusted with the results shown in Table 7.  
To further shorten the total working time and improve the guaranteed efficiency, the guarantee vehicles with fewer working hours and long idle time should be appropriately reduced, and the key vehicles affecting the working hours should be appropriately increased. Considering the actual situation of the airport support, after further optimizing the number of support vehicles, the operation scheduling results of the support vehicles  
Figure 6: Multi-aircraft support three times before and after scheduling optimization. Figure 10: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Vehicle** & **Aircraft** & **3** & **4** & **5** & **6** \\ \hline Re & 2 & 2 & 3 & 3 \\ Ox & 1 & 1 & 2 & 2 \\ Po & 1 & 1 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Number of support vehicles for a certain type of aircraft. Figure 11: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline
**Aircraft** & **Vehicle** & \(\mathbf{T_{o}}\) & \(\mathbf{T_{w}}\) & **Utilization Ratio** & \(\mathbf{T_{t}}\) \\ \hline \multirow{4}{*}{3} & Re1 & 30 & 0 & 100\% & \multirow{4}{*}{60} \\  & Re2 & 60 & 0 & 100\% & \multirow{4}{*}{60} \\  & Ox & 45 & 15 & 75\% & \\  & Po & 30 & 25 & 54.5\% & \\ \hline \multirow{4}{*}{4} & Re1 & 60 & 0 & 100\% & \multirow{4}{*}{60} \\  & Re2 & 60 & 0 & 100\% & \\  & Ox & 60 & 0 & 100\% & \\  & Po & 40 & 15 & 72.7\% & \\ \hline \multirow{4}{*}{5} & Re1 & 30 & 0 & 100\% & \multirow{4}{*}{65} \\  & Re2 & 60 & 0 & 100\% & \\  & Re3 & 60 & 0 & 100\% & \\  & Ox1 & 30 & 15 & 66.7\% & \\  & Ox2 & 45 & 15 & 75\% & \\  & Po & 50 & 15 & 76.9\% & \\ \hline \multirow{4}{*}{6} & Re1 & 60 & 0 & 100\% & \multirow{4}{*}{60} \\  & Re2 & 60 & 0 & 100\% & \\  & Re3 & 60 & 0 & 100\% & \\ \cline{1-1}  & Ox1 & 45 & 15 & 75\% & \\ \cline{1-1}  & Ox2 & 45 & 0 & 100\% & \\ \cline{1-1}  & Po1 & 30 & 25 & 54.5\% & \\ \cline{1-1}  & Po2 & 30 & 25 & 54.5\% & \\ \hline \end{tabular}
\end{table} 
Table 8: Support vehicles utilization and completion time of three operations. Figure 10: Change of population _fitness_ value. (**a**) 3 aircraft, (**b**) 4 aircraft, (**c**) 5 aircraft, (**d**) 6 aircraft.