## 1 Introduction
[11] constructed a mathematical model of open pit mine transportation scheduling to minimise the transportation cost. They solved it using an adaptive fruit fly optimisation algorithm, effectively reducing transportation costs. [PERSON] et al. [12] investigated the time-window scheduling model for the vehicle path problem, and through practical genetic algorithms and set partitioning formulas, they proposed a distance travelled as the main objective of the VRPTW robust heuristic to shorten the minimum travel distance, which outperforms all previously known and published heuristics. [PERSON] et al. [13] studied station siting and mixed fleet sizing problems based on a minimum cost flow model considering CO2 emission constraints. [PERSON] et al. [14] proposed an optimisation problem for multidimensional complex spaces based on the fireworks algorithm (FWA) and the grey wolf optimisation algorithm, which merges the superior developmental capabilities of the grey wolf algorithm with the robust exploratory prowess of FWA. Thirty tests were conducted on 16 benchmark functions using swarm intelligence algorithms such as the improved particle swarm algorithm, crow search algorithm, mothballing algorithm, and the proposed hybrid algorithm. The performance advantages of the hybrid algorithm are verified. [PERSON] et al. [15] studied the multi-story warehouse layout problem under uncertainty, where uncertain variables describe monthly demand and horizontal transportation distance. The relationship between the opportunity-constrained planning model and the opportunity maximisation planning model is discussed, which provides an effective method for finding the optimal solution for the opportunity maximisationplanning model. The hybrid vehicle path optimisation model studied by [PERSON], [PERSON] et al. [16] incorporates a fleet allocation problem and sets up different cost calculation formulas for the differences between the two models. The influence of charging facilities on the planning results is also considered. The ant colony algorithm is improved and integrated with the idea of decentralised search, combined into a two-stage heuristic algorithm. The algorithm's performance is demonstrated by comparing the proposed algorithm with the Cplex solver. The shortest path planning problem for mobile equipment, including auxiliary vehicles in underground coal mines, was investigated using differential evolutionary algorithms by [PERSON] [17] and others. [PERSON] et al. [18] constructed a multi-objective scheduling optimisation model. They introduced a multi-objective optimisation algorithm, NNIA, to solve the problem by comprehensively considering various constraints, such as the production capacity of the car shovels, the ore grade, and the vehicles' loading capacity. [PERSON] et al. [19] proposed a hybrid vehicle scheduling model that distinguishes between different vehicle types and considers factors such as traffic congestion, load limits, and driving distances. This model employs genetic and simulated annealing algorithms for solving the problem and experimentally compares the performance of these two algorithms. While these algorithms have partially resolved the transportation scheduling issues in surface coal mines, challenges remain, including limited global optimisation capabilities and the need for extensive parameter tuning. Open pit mining card scheduling optimisation is mainly implemented using heuristic algorithms, genetic algorithms, annealing algorithms, grey wolf optimiser [20]. Genetic algorithms can effectively solve multi-objective optimization problems and have strong global optimization seeking ability [21]. The grey wolf optimisation (GWO) algorithm is characterised by robust convergence, minimal parameter requirements, straightforward programming implementation, and a strong local search capability [22]. GWO has excellent results in solving combinatorial optimisation problems such as vehicle scheduling, path planning, and job shop scheduling problems [23; 24; 25].