## 4 Simulation and Analysis
### Parameter-Setting Analysis  
In this section, we study the influence of different weight settings on the model output results. We set \(\omega_{1}\), \(\omega_{2}\), \(\omega_{3}\) and took 0.1 as the step size, then set 36 groups of control tests, where \(\omega_{1}+\omega_{2}+\omega_{3}=1\). The experiment was repeated 30 times in each group, and the results were averaged. The costs of the upper-layer model vary with the weight as shown in Figure 14. It can be observed that each cost has different sensitivities to different weights, and there is no single right reorganization. The Pareto optimal solution of each weight-recombination category is selected as shown in Table 9, which can be roughly divided into two categories. The first five groups, that is, when \(\omega_{1}\leq 0.4\), tend to exchange a higher selected station count for a lower average picking distance and a lower picking distance difference of unit demand. The last five groups, that is, when \(\omega_{1}\geq 0.5\), tend to exchange a higher average picking distance and picking distance difference for a lower selected station count. On the one hand, according to the optimization objective Function (17), it can be seen that the lower-layer model tends to have a better performance in the location scheme with a lower station count. Therefore, after the inclusion of the lower model, the algorithm may be more inclined to choose the scheme with a smaller station count. On the other hand, a high selected station count is not conducive to the quick solution of the lower-layer model, and the specific analysis will be given in Section 4.4. Therefore, among the five solutions of \(\omega_{1}\geq 0.5\), weight group 8 (0.6, 0.2, 0.2) with the most balanced performance among the three costs of the upper-layer model is selected as the weight combination of the upper-layer model. Further study of the weight proportion of the upper-layer model and the lower-layer model after determining the weight proportion of the three costs of upper-layer model was conducted. We set \(\varphi_{1}\) and \(\varphi_{2}\), took 0.1 as the step size, and set nine groups of control tests, where \(\phi_{1}+\phi_{2}=1\). The experiment was repeated 30 times in each group, and the  
Figure 14: Cost of different upper-layer model-weight combinations. (**a**) Selected parcel-receiving station count. (**b**) Average picking distance. (**c**) Picking fairness. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **Weight Scale** & \begin{tabular}{c} **Parcel-Receiving** \\ **Station Count** \\ \end{tabular} & \
\begin{tabular}{c} **Average Picking** \\ **Distance** \\ \end{tabular} &
\begin{tabular}{c} **Standard Deviation of** \\ **Picking Distance** \\ \end{tabular} \\ \hline
1 & 0.1, 0.6, 0.3 & 62.0 & 31.08 & 36.87 \\
2 & 0.3, 0.4, 0.3 & 50.8 & 34.67 & 36.29 \\
3 & 0.4, 0.1, 0.5 & 52.0 & 35.00 & 36.17 \\
4 & 0.4, 0.2, 0.4 & 52.0 & 35.14 & 36.14 \\
5 & 0.4, 0.4, 0.2 & 55.0 & 33.29 & 36.52 \\
6 & 0.5, 0.2, 0.3 & 17.5 & 108.00 & 59.81 \\
7 & 0.5, 0.3, 0.2 & 17.0 & 108.02 & 57.31 \\
8 & 0.6, 0.2, 0.2 & 17.0 & 100.78 & 61.32 \\
9 & 0.6, 0.3, 0.1 & 17.0 & 97.42 & 63.38 \\
10 & 0.8, 0.1, 0.1 & 17.0 & 97.03 & 64.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Pareto optimal solution with different weight combinations. results were averaged. Based on the layered solution results of the parcel-receiving station location and the public air-route planning, the cost changes of each weight combination are shown in Table 10. It is observed that there is no obvious change trend among the weight combinations, but the cost of public air-route operation of each weight combination is better than those of the layered solution results. After adding the lower-layer model, the public route planning affects the location scheme of the upper-layer model. The average selected station count increased, which also resulted in a decrease in the average picking distance and picking distance variance.