## 4 Instance Analysis  
To verify the effectiveness of the hybrid GA-GWO algorithm applied in a real scenario of scheduling container transportation vehicles in surface coal mines., the actual transportation scheduling data of container vehicles of a coal mine on a particular day are taken as an example, and the GA-GWO hybrid algorithm is applied to solve the optimisation model of coal mine container vehicle scheduling. The performance of the SA, GA, and GWO algorithms is compared and analysed using this hybrid approach. empty loads is 22 and 34 km/h, respectively. The average speed of the vehicles travelling with heavy and empty loads is 22 and 34 km/h, respectively. GA-GWO parameter settings: The search space dimension is 28, the initial population size is 20, the number of evolutionary iterations is 2000, the maximum number of elite iterations is 500, and the crossover probability and variance probability are 0.6 and 0.1, respectively. GA parameter settings refer to the literature [33], while GWO algorithm parameter settings refer to the literature [8]. To reduce the algorithm's complexity in time, this paper directly takes the objective function as the fitness function. It uses Matlab 2022b to simulate and solve the problem of scheduling coal mine container vehicles, and the evolution curve is shown in Figure 5. Figure 5 illustrates that, compared to SA, GWO, and GA, the GA-GWO hybrid algorithm shows an improvement of 43.1%, 46.2%, and 43.7% in optimisation speed, respectively. The optimisation accuracy is enhanced by 4.12%, 6.1%, and 3.2%, respectively. Furthermore, the evolution curve of GA-GWO exhibits rapid changes prior to 500 iterations and stabilises thereafter. The evolutionary curve of GA-GWO changes rapidly before 500 iterations and stabilises after 500 iterations. This is because GA-GWO inherits the strong computational ability and global search ability of GA in the early stage, which makes the population converge to the global optimum quickly; in the middle and late stages, the addition of the GWO algorithm allows the population to concentrate around the most optimal population quickly, which reduces the probability of GA falling into the slow convergence in the middle and late stages and thus balances the global optimum with the local optimum. The population evolution curve of the algorithm includes two indicators, the best objective value and the average objective value, and the population evolution curve is shown in Figure 6. In SA, the randomness of the initial value causes the algorithm to fall into a local optimal solution in the initial stage, which increases the optimisation difficulty and running time during the iteration process. In GA, the best and average objective values converge gradually with the number of iterations. However, as the genetic algorithm proceeds, due to the failure to utilise the feedback information from the network promptly, the search speed of the later algorithm slows down, and more training time is required to obtain a more accurate solution. In GWO, the grey wolves search and evolve with each other to find a better solution, and the whole population grows towards a better objective function. Because of the significant dependence on the initial solution, the quality of the initial solution will directly affect the algorithm's convergence, and the final result will cause the algorithm to fall into the local optimal solution. The optimised GA-GWO hybrid algorithm integrates the strengths of both the genetic and grey wolf optimisation algorithms. It demonstrates potentially faster convergence and improved outcomes for both optimal and average objective values on the population evolution curve. By employing the genetic algorithm's crossover and mutation operations, combined with the search strategy of the grey wolf optimisation algorithm, the algorithm enhances its global and local search capabilities during evolution. Consequently, this comprehensive approach allows the algorithm to converge more efficiently to the global optimum solution.