# A Multi-Customer Vehicle Scheduling Optimization Method for Coal Intelligent Loading System
## 5 Case Verification
### Vehicle Scheduling Plan  
The data in Table 5 are plugged into the established vehicle scheduling model, Formulas (8) to (20), and the model is solved by the enhanced SSA to generate the vehicle scheduling plan. (1) Parameter Setting  
The model is solved in MATLAB 2019. The initial values and units of each parameter of the model are set as shown in Table 11. The algorithm parameters are set as shown in Table 12.  
Some of the key parameters and behaviors of the SSA are described in Table 12. This algorithm searches using a population of 300 sparrows for a maximum of 150 iterations per iteration. The algorithm will issue an alert when the searched target value is greater than or equal to 0.6. In each iteration, 70% of the sparrows will follow the optimal strategy for the search, while 20% of the sparrows will remain vigilant to avoid falling into local optimal solutions. In addition, there is a 0.9% probability for two sparrows to cross over and a 1% probability for each sparrow to mutate. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \begin{tabular}{c} **Customer Orders** \\ \end{tabular} & \begin{tabular}{c} **X\({}_{1}\)** \\ \end{tabular} & \begin{tabular}{c} **X\({}_{2}\)** \\ \end{tabular} & \begin{tabular}{c} **X\({}_{3}\)** \\ \end{tabular} & \begin{tabular}{c} **X\({}_{4}\)** \\ \end{tabular} & \begin{tabular}{c} **X\({}_{5}\)** \\ \end{tabular} & \begin{tabular}{c} **Group** \\ \end{tabular} & \begin{tabular}{c} **Individual** \\ \end{tabular} & \
\begin{tabular}{c} **Interest** \\ **Ratio** \\ \end{tabular} &
\begin{tabular}{c} **Order** \\ **Ranking** \\ \end{tabular} \\ \hline
1 & 0.8 & 0.6 & 0.6 & 0.4 & 0.4 & 2.85 & 0.75 & 0.6719 & 5 \\
2 & 0.8 & 0.8 & 0.4 & 0.4 & 1 & 3.9 & 1 & 0.9062 & 6 \\
3 & 0.4 & 0.6 & 0.6 & 0.8 & 0.6 & 2.15 & 0.6 & 0.5239 & 4 \\
4 & 0.2 & 0 & 1 & 1 & 0 & 0 & 0 & 1 \\
5 & 1 & 1 & 0.2 & 0 & 0.8 & 4.8 & 1 & 1 & 7 \\
6 & 0.2 & 0.4 & 1 & 0.8 & 0.2 & 0.8 & 0.4 & 0.2833 & 2 \\
7 & 0.2 & 0.4 & 0.8 & 0.8 & 0.4 & 1.25 & 0.4 & 0.3302 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Group benefit value, individual regret value, interest ratio, and order ranking. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Customer Orders** \\ \end{tabular} & \begin{tabular}{c} **Evaluation Indicators** \\ \end{tabular} & \begin{tabular}{c} **X\({}_{1}\)** \\ \end{tabular} & \begin{tabular}{c} **X\({}_{2}\)** \\ \end{tabular} & \begin{tabular}{c} **X\({}_{3}\)** \\ \end{tabular} & \
\begin{tabular}{c} **X\({}_{4}\)** \\ \end{tabular} &
\begin{tabular}{c} **X\({}_{5}\)** \\ \end{tabular} \\ \hline
1 & 0.2 & 0.4 & 0.5 & 0.4 & 0.6 \\
2 & 0.2 & 0.2 & 0.25 & 0.4 & 0 \\
3 & 0.6 & 0.4 & 0.5 & 0.8 & 0.4 \\
4 & 0.8 & 1 & 1 & 1 & 1 \\
5 & 0 & 0 & 0 & 0 & 0.2 \\
6 & 0.8 & 0.6 & 1 & 0.8 & 0.8 \\
7 & 0.8 & 0.6 & 0.75 & 0.8 & 0.6 \\ \hline \begin{tabular}{c} Each index positive \\ and negative ideal \\ solution \\ \end{tabular} & \
\begin{tabular}{c} Positive ideal \\ solutions \\ \end{tabular} & 0.8 & 1 & 1 & 1 & 1 \\ \cline{2-6} solution &
\begin{tabular}{c} Negative ideal \\ solutions \\ \end{tabular} & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Normalization treatment and table of positive and negative ideal solutions. (3) Scheduling Plan  
According to the parameter settings in Tables 11 and 12 above, and the example data of coal mining enterprises in Table 5, the model is solved using the CCM-SSA algorithm. According to the iteration graph shown in Figure 8, the total cost of the scheduling plan gradually decreases as the number of iterations increases. The CCM-SSA algorithm converges after about 57 iterations, reducing the total cost of the scheduling plan to CNY 9012. This result demonstrates the capability of the CCM-SSA algorithm to generate outcomes in fewer iterations. The loaded scheduling plan after optimization is generated by the CCM-SSA algorithm, as depicted in Table 13.  
According to Table 13, the loading scheduling plan for the three loading bunkers of the enterprise can be observed.