## 1 Introduction  
As an important part of the mine production, the auxiliary transportation system of the coal mine undertakes important transportation tasks such as pulling gangue, feeding, coal transportation, and transporting people. In recent years, with the rapid development of technology, China's open-pit coal mines have significantly increased the level of information, intelligence, and automation in the transportation link. However, most complex geological underground coal mine enterprises still use manual scheduling methods for transportation scheduling. The development of intelligent and less humanized levels is relatively flat, which has become a key link limiting the intelligent construction of underground coal mines. In 2019, the State Administration of Coal Mine Safety included handling robots and underground unmanned transport vehicles in the 'Key R & D Catalogue for Coal Mine Robots', rapidly promoting the intelligent construction of mine transport robots. The research and development of the unmanned underground transportation robot provide a guarantee for the intelligent development of the auxiliary transportation of underground mining. Studying the intelligent scheduling technology of the whole mine material, taking the demand as the guide, scientifically organizing the transportation activities, optimizing the material loading strategy, and arranging the distribution robot reasonably can reduce the waste of resources, improve labor productivity, increase income, and accelerate the transformation of mining enterprises. At present, the material loading method and loading sequence are mainly obtained by manual experience. This manual loading is often loaded one by one according to the materials on the loading list. The operation workload is large, the loading efficiency is low, the safety is low, and the space utilization rate of the standard container is low. Since the emergence of the cutting inventory problem in the 1960s, the solution to the three-dimensional loading problem has developed rapidly. Many researchers have proved that the hybrid genetic algorithm, heuristic method, and differential evolution (DE) algorithm can solve the heterogeneous material loading problem [1; 2; 3]. To increase the practicability of the loading algorithm, some scholars have studied the relationship between the constraints related to the box direction, load stability, and box separation, which greatly reduces the variables and constraints contained in the mathematical model and increases the solution efficiency [4; 5; 6]. [PERSON] et al. [7] developed an extended priority-based hybrid genetic algorithm (EP-HGA) to determine the loading mode for the problem of transporting a small number of items that cannot fully utilize the container space. At present, most of the three-dimensional loading problems use heuristic algorithms to provide solutions, but most of the research focuses on single-container loading of strong heterogeneous items or multi-container loading of large-scale weak heterogeneous items, and there are few studies on three-dimensional loading algorithms for large-scale heterogeneous items. Vehicle scheduling usually plays an important role in intelligent transportation and vehicle route planning [8; 9]. In the distribution process, first of all, ensuring that vehicles operate without conflict [10] and the realization of transport equipment sharing can greatly improve the efficiency of distribution and the scheduling cost savings [11]. Vehicle route planning usually solves the shortest distance to reduce time and fuel costs, but the increase in the number of vehicles on demand will greatly increase the total cost. The scheduling optimization based on unmanned transport robots needs to optimize two objectives, namely, the shortest distance and the minimum number of vehicles. This can be considered a multi-objective programming model. The key to solving such problems is to find Pareto optimality [12].