## 4 Simulation and Analysis
### Results of Analysis  
According to the above simulation scenarios and simulation data, the logistics UAV parcel-receiving station and public air-route planning model is solved. All experiments were performed on a server with 4.7 GHz Intel-Core i7 processors, 16 GB RAM, with Windows Server 11 Operating System and the maximum solution time set to 3600 s. We repeated the experiment for 30 groups. The average solution time was 2352 s, and we selected one group to display the best solution. The solution results of the bi-layer model are shown in Figure 11, and Tables 7 and 8. The number of parcel-receiving station locations is 17, the average picking distance is 98.10 m, and the picking distance standard deviation of unit demand is 49.22 m. Each parcel-receiving station matched two to ten demand points. Among them, 12 package-receiving stations have matching demand between 2000 and 5000. There are three parcel-receiving stations with a demand of less than 2000 and one parcel-receiving station with a demand of more than 10,000. The public air routes include 17 edges, forming 17 routes connecting the warehouse and stations, including four direct routes and 13 transit routes. After optimization, the air route between any two points is an almost straight line between two points, and some routes have a small amount of bends due to obstacle avoidance or high comprehensive risk. \begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Maximum UAV air range \(L_{\text{max}}/\text{m}\) & 3000 & Minimum cost-scaling factor \(k\) & 1.5 \\ Maximum UAV turning angle \(\theta_{\text{max}}\) & \(\pi/2\) & Upper-layer model fitness weight \(\varphi_{1}\) & 0.5 \\ Maximum negative route optimization \(\gamma\) & 20\% & Lower-layer model fitness weight \(\varphi_{2}\) & 0.5 \\ Maximum acceptable picking distance \(d_{\text{max}}/\text{m}\) & 200 & Parcel-receiving station count weight \(\omega_{1}\) & 0.6 \\ Grid width/m & 5 & Average picking distance weight \(\omega_{2}\) & 0.2 \\ Maximum picking distance point \(d_{\text{max}}/\text{m}\) & 200 & Picking fairness weight \(\omega_{3}\) & 0.2 \\ Maximum parcel-receiving station count \(N_{\text{s}}^{\text{max}}\) & 74 & Pheromone volatilization factor \(\rho\) & 0.95 \\ Maximum parcel-receiving station capacity \(C_{\text{max}}\) & 15,000 & Pheromone factor \(\alpha\) & 1.0 \\ Minimum parcel-receiving station capacity \(C_{\text{min}}\) & 1000 & Heuristics factor \(\beta\) & 1.0 \\ GA population size \(N^{U}\) & 50 & Ant colony size \(m\) & 50 \\ GA maximum iterations \(N_{\text{max}}^{\text{GA}}\) & 200 & MMAS maximum iterations \(N_{\text{max}}^{\text{MMAS}}\) & 200 \\ Parcel-receiving station scaling \(\chi\) & 0.5 & MMAS early-stop iteration \(N_{\text{ES}}^{MMAS}\) & 20 \\ \hline \end{tabular}
\end{table} 
Table 6: Simulation parameter settings. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Station** & **Demand Point** & **Demand** & **Station** & **Station** & **Demand Point** & **Demand** & **Station** \\
**Number** & **Volume** & **Coordinates** & **Number** & **Volume** & **Coordinates** \\ \hline
1 & 6 & 2052 & \((28,174)\) & 10 & 5 & 2886 & (221, 157) \\
2 & 10 & 3121 & \((66,211)\) & 11 & 3 & 2943 & (229, 110) \\
3 & 8 & 2334 & \((116,214)\) & 12 & 2 & 2320 & (266, 115) \\
4 & 5 & 2154 & \((99,180)\) & 13 & 4 & 3568 & (270, 180) \\
5 & 2 & 1125 & \((95,158)\) & 14 & 5 & 2046 & (201, 193) \\
6 & 4 & 11668 & \((123,109)\) & 15 & 5 & 1140 & (230, 213) \\
7 & 4 & 1807 & \((106,82)\) & 16 & 3 & 5309 & (244, 60) \\
8 & 2 & 3561 & \((145,41)\) & 17 & 3 & 4703 & (276, 63) \\
9 & 3 & 3050 & \((112,33)\) & & & \\ \hline \end{tabular}
\end{table} 
Table 7: Location results of parcel-receiving stations. Compared with the public air route combined with the direct route (the results of the single-air-route planning) for all stations, the total risk is significantly reduced by 43.30%, and the cost of public air-route operation is reduced by 5.17%.