## 1 Introduction
[PERSON]'s hybrid NSGA-II, [PERSON] IR-NSGA-III, [PERSON]'s AGPSO, and [PERSON]'s decomposition-based multi-objective evolutionary algorithm are more efficient than general optimization algorithms [13; 14; 15; 16]. Hybrid particle swarm optimization and NSGA2 algorithms are effective methods for solving multi-objective programming problems [17]. [PERSON] [18] introduced a replication strategy based on immune density in the DPSO algorithm, which avoided the premature convergence problem and improved the ability to search for the global optimal solution. [PERSON] [19] integrated the strategy of an artificial fish swarm algorithm into the position update process of particle swarm optimization (PSO), which reduced the total execution time of the application. [PERSON] [20] integrated weight aggregation into MOPSO, which improved the efficiency of generating the Pareto frontier. [PERSON] [21] combined a fast non-dominated genetic algorithm and time-varying multi-objective particle swarm optimization for global optimization and discussed the influence of particle numbers on optimization results by changing the population size. [PERSON] [22] introduced the idea of multivariate multi-objective co-evolution on the basis of multi-objective genetic programming to deal with multiple scheduling decisions at the same time. [PERSON] [23] used the weight aggregation method to constrain the multi-objective mathematical model on the basis of the NSGA-II algorithm to obtain the Pareto optimal solution set faster. [PERSON] [24] used the k-nearest neighbor algorithm to improve the genetic algorithm to achieve dynamic multi-objective programming. [PERSON] [25] proposed a hybrid multi-objective genetic algorithm based on a two-generation and elite strategy to improve computational efficiency. Scheduling is the process of adding start and finish information to the job order specified by the order [26]. An intelligent scheduling system usually includes three-dimensional loading and path planning. It is usually identified as a three-dimensional loading capacity vehicle routing problem, which is a kind of high complexity and difficult problem to solve. Scholars have used numerical experiments to prove that the hybrid method of genetic algorithm and tabu search, the adaptive large neighborhood search of routing and the different packing heuristic algorithms for the loading part, the tabu search algorithm and the tree search algorithm for loading, the improved minimum waste heuristic algorithm, the improved genetic algorithm, ant colony optimization algorithm, branch cutting algorithm,tabu search, and multi-starting point evolution strategy can give a feasible solution to the three-dimensional loading capacity vehicle routing problem [27; 28; 29; 30; 31; 32; 33; 34; 35]. Most of the data used in most studies are benchmark instances in daily life. Since the benchmark instances mostly conform to the law of open-air loading and distribution, it is still unknown whether the method in the study is feasible for underground material scheduling in coal mines. Aiming at the problems being multipoint and having long length, low intelligent distribution, high working intensity, and high risk of underground coal mine auxiliary transportation system, an intelligent scheduling strategy for whole mine materials is proposed. Based on the demand information, auxiliary transportation equipment, and line status, a multi-objective and multi-object scheduling optimization model is established. With the help of dynamic topology technology, the intelligent distribution and auxiliary transportation path optimization of the whole mine materials are realized. Firstly, combined with the characteristics of materials and standard containers, a three-dimensional loading model is established with the goal of maximizing the space utilization of standard containers, and a three-dimensional space segmentation heuristic algorithm is used to solve the material loading scheme.