# A Multi-Customer Vehicle Scheduling Optimization Method for Coal Intelligent Loading System
## 5 Case Verification
### Vehicle Scheduling Plan
The specific plan is represented as follows: loading bunker 1 starts at 8:00 a.m. and first loads 4-1 with a loading time of 13 min, ending loading at  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Loading Bunker Serial Number** & \multicolumn{5}{c}{**Loading Plan**} \\ \hline  & 4-1(13) \(\underset{813}{\rightarrow}\) 3-1(10) \(\underset{823}{\rightarrow}\) 2-4(10) \(\underset{833}{\rightarrow}\) 4-5(13) \(\underset{846}{\rightarrow}\) 3-2(10)  
8:13 a.m., and then loads 3-1. The expected arrival loading time for 2-7 is between 11:00 and 11:30, but the end time of coal loading is 11:31, which is 1 min later than the expected arrival loading time, resulting in penalty costs. According to the loading plan mentioned above, the loading operation notification table is obtained. Due to the large amount of data, the loading operation notification customer 1 is shown in Table 14 as a representative, which has nine vehicles. Table 14 specifies that the loading operation started at 8:47 under loading bunker 3 for 1-1. After 12 min of loading coal, the operation was completed, and the business left at 8:59. (4) Comparison Before and After Optimization  
The total loading delay time of each customer before and after optimization was compared, as shown in Table 15.  
According to Table 15, a total of five customer orders were delayed before optimization, with a total delay time of 70 min. Two customer orders were delayed after optimization, totaling a delay of 19 min. The plans before and after optimization are compared, and the comparison results of each cost and total cost are shown in Figure 9.  
It can be observed from Figure 9 that the operating cost before and after optimization is CNY 3600. However, the carbon emission cost after optimization was CNY 412, which was reduced by CNY 1105, or 72.8%, compared to CNY 1517 before optimization. The penalty cost after optimization was CNY 5000, compared to CNY 37,750 before optimization, resulting in a decrease of CNY 32,750, which represents an 86.8% decrease. Overall, the total cost of the optimized plan is reduced by CNY 33,855, or 79%, compared to the pre-optimization plan. Therefore, the scheduling plan addressed by the MCVS model can offer enterprises loading operation notifications, enabling them to efficiently accomplish vehicle loading tasks and effectively reduce overall costs. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Customer Number**} & \multicolumn{3}{c}{**Loading Delay Time**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Before Optimization**} & \multicolumn{2}{c}{**After Optimization**} \\ \hline
1 & 23 & - \\
2 & 13 & 1 \\
3 & - & - \\
4 & - & - \\
5 & 29 & 18 \\
6 & 4 & - \\
7 & 1 & - \\ Total & 70 & 19 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Comparison of the customers total loading delay time before and after optimization. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Loading Bunker**} & \multirow{2}{*}{**Customer**} & \multirow{2}{*}{**Vehicle**} & \multirow{2}{*}{**Load Capacity**} & \multirow{2}{*}{**Loading Start**} & \multirow{2}{*}{**Loading End**} \\  & & & & \(Q_{\bar{q}}\) **(ton)** & & **Time \(t_{\mathrm{s}}\)** & **Time \(t_{\mathrm{e}}\)** \\ \hline
3 & 1 & 1 & 12 & 8:47 & 8:59 \\
1 & 1 & 2 & 12 & 9:18 & 9:30 \\
2 & 1 & 3 & 12 & 9:17 & 9:29 \\
3 & 1 & 4 & 8 & 9:37 & 9:45 \\
1 & 1 & 5 & 12 & 8:56 & 9:08 \\
3 & 1 & 6 & 8 & 10:29 & 10:37 \\
1 & 1 & 7 & 12 & 11:18 & 11:30 \\
2 & 1 & 8 & 12 & 10:38 & 10:50 \\
3 & 1 & 9 & 8 & 9:58 & 10:06 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Notification of loading operation before scheduling. The MCVS optimization method of intelligent coal loading system proposed in this paper provides a new way for coal mining enterprises to transform into intelligent operations. Although the MCVS method proposed in this paper uses enhanced customer priority and the sparrow search algorithm to obtain an optimized vehicle scheduling plan, there are still some challenges and limitations in practical application.