## 4 Instance Analysis
Figure 6: Population evolution curve. The waiting time of container vehicles serves as one of the objective functions, and the comparative waiting times under the three algorithms are displayed in Figure 7. GA selects 'excellent' individuals based on fitness function results and performs further operations on them. Through many iterations, SA calculates the objective function difference based on random selection within the domain of the current solution. The waiting time of container transportation vehicles is reduced to a certain extent by the number of iterations after the temperature is reduced to a certain threshold. GA will gradually optimise the vehicle scheduling; we can see that the waiting time of 30 vehicles in the last batch is getting less and less, and GWO searches and adjusts by simulating the collaborative and competitive behaviours of grey wolves and gradually converges to the optimal solution, which can effectively find the solution with less waiting time in the vehicle scheduling scheme. However, some vehicles still have a long waiting time. The GA-GWO hybrid algorithm considers both the global search and the optimisation of the process. The process finds both worldwide search and local search capabilities. Through the crossover and mutation operations of the genetic algorithm and the search strategy of the grey wolf optimisation algorithm, the solution space can be searched more finely, and the overall waiting time of the container transport vehicles can be found to be an optimised solution with less waiting time. Figure 7: Container transport vehicle waiting time. Container reversal time is an essential indicator of the model. The container reversal time of the three algorithms is shown in Figure 8. SA does not have a more reasonable scheduling optimisation for subsequent batches of vehicles after falling into a local optimal solution in the previous period. GA may be unable to optimise the subsequent batches' transportation order, leading to an extended overall container reversal time. GWO may have a locally optimal solution problem in specific cases. In the following batches, there may be a possibility that some vehicles are not reasonably scheduled, which leads to a relatively extended container reversal time. The GA-GWO hybrid algorithm can better optimise the vehicle transportation order and make the overall container shortest time. In summary, after analysis, the GA-GWO hybrid algorithm solved the container reversal-time problem better. In practical applications, the GA-GWO hybrid algorithm is chosen to optimise vehicle scheduling to minimise the short backward time and improve the efficiency of container transportation. In the GA-GWO hybrid algorithm, 30 container vehicles are dispatched in a maximum batch of 7 times, and the results of vehicle dispatching are shown in Table 5.  
Figure 8: Container reversal time. In the actual container vehicle scheduling, one shift requires 30 transport vehicles; combined with the specific parameters of the coal mine, the exact total cost of a change in container reversal operation is 9121.6 RMB, the reversal time is 72 h, and the container reversal completion time is 3.2 h, respectively, based on the SA, GA, GWO, and GA-GWO solutions for coal mine container transportation. The total cost of vehicle scheduling, container-to-short time, and container-to-short plan completion time are shown in Table 6.  
Table 5 shows that, compared with the actual transportation data, the total cost solved by the SA, GA, and GWO algorithms within the same shift only saves 327.171 RMB, 484.74 RMB, and 173.6 RMB, respectively. In contrast, the total cost solved by the hybrid GA-GWO algorithm saves 675.31 RMB, and the container's backward time and planned completion time save ten h and 0.63 h.