## 4 Results and Discussion  
### Optimal Weight Coefficients  
The multi-objective function serves as the basis for evaluating the quality of solutions in the genetic algorithm, and the weight coefficients assigned to the two objectives directly impact the fitness evaluation of the solutions. To balance the weight coefficients of the two scheduling objectives and establish an optimal evaluation standard, thereby achieving the best solution, this study established five weight schemes for the following two objectives: the total transportation cost and the total waiting time. Based on the above, we plan to attempt to establish five different schemes. In Scheme 1, the weight for the total transportation cost is set to one and for total waiting time to zero. In Scheme 2, the weight for the total transportation cost is seven-tenths and for the total waiting time is three-tenths. Scheme 3 assigns equal weights to both objectives, with five-tenths for the total transportation cost and five-tenths for the total waiting time. Scheme 4 swaps the weights of Scheme 2, setting the weight for the total transportation cost at three-tenths and for the total waiting time at seven-tenths. Finally, Scheme 5 reverses the emphasis, assigning a weight of zero to the total transportation cost and one to the total waiting time. The scheduling results under these five weight distribution schemes are presented in Table 5.  
The experimental results for the five weight distribution schemes are shown in Figure 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Unloading Point** & & & & \\ \hline
**Exchange Station** & & & & & \\ \hline
0 & 0.3 & 2.4 & 2.4 & 1.4 \\
1 & 1.3 & 1.0 & 1.4 & 1.9 \\
2 & 2.5 & 2.4 & 0.3 & 0.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The best transportation route between the unloading point and the substation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Shovel Point** & & & & \\ \hline
**Exchange Station** & & & & & \\ \hline
0 & 1.2 & 1.4 & 2.0 & 1.5 & 2.6 \\
1 & 1.9 & 1.8 & 1.2 & 0.1 & 2.4 \\
2 & 1.0 & 2.7 & 2.4 & 1.3 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The best transportation route between the shovel point and the exchange station. Figure 2: Experimental results for the five weight distribution schemes. According to Table 7, the sensitivity analysis of the total transportation cost and total waiting time based on different weight coefficients is shown in Figure 3.  
Based on Figure 3, the sensitivity analysis across the different weight schemes is displayed as follows: (a) shows a progressive increase in the total transportation cost from Scheme 1 to Scheme 5; (b) reflects a decline in the waiting time as the scheme shifts from Scheme 1 to Scheme 5; (c) shows that the total distance traveled increases incrementally from Scheme 1 through Scheme 5, indicating higher utilization in terms of the distance covered; (d) shows that there is a gradual increase in the number of battery changes required from Scheme 1 to Scheme 5. From these observations, Scheme 3, which allocates equal weights of five-tenths to both the transportation cost and waiting time, emerges as the optimal balance between these objectives. This balanced weighting minimizes the overall costs and waiting time, suggesting that Scheme 3 provides the most efficient scheduling model. Consequently, Scheme 3 has been selected for further analysis in this study due to its balanced performance in optimizing both cost and efficiency metrics.