# Research on Selection and Matching of Truck-Shovel in
###### Abstract  
The equipment investment is large and the annual operating cost is high in oversized open-pit mines. The rationality of the truck-shovel selection and matching has a more prominent influence on the economic benefit of mines. Therefore, we set up an optimization method of truck-shovel selection for oversized open-pit mines, and developed a software system of truck-shovel matching to solve the problem of truck-shovel selection and matching for oversized open-pit mines. First, based on the likelihood statistics method to decide the reasonable quantity range of shovels in the oversized open-pit mine, the selection and matching of shovels under different production scales are realized according to the production ability equation of shovels. Then, according to the principal of deciding the truck by shovel, and by deciding the shovel equipment, the truck-shovel efficiency model is constructed, the proper bucket-to-capacity ratio is obtained in the oversized open-pit mine, and the economic range of truck matching under different shovel specifications is determined by the bucket-to-ability ratio. Finally, using this method, the software system of truck-shovel matching is developed by using C# language, which can automatically calculate the quantity of equipment and the material consumption. The results show that the reasonable number of shovels for the oversized open-pit mine should be 4-7, and the bucket-to-capacity ratio should be 4-5:1. When the annual excavation quantity of the mine is 80-150 million tons, the 35-55 m\({}^{3}\) shovels should be the mainstream specification. The research results were applied to a new oversized open-pit mine in Tibet, China, and the problem of equipment selection was solved. oversized open-pit mine; truck-shovel selection; bucket-to-capacity ratio +
Footnote †: journal: Medical  
The traditional methods of mine equipment selection in China are mainly the analogy and experience method, which are selected by comparing the statistical data of shovel matching of existing production mines [2]. With the background of the diversified development of mine equipment, the traditional method has lost its superiority. To choose a more economical equipment combination scheme, a more refined equipment selection and matching method is needed. In recent years, scholars all over the world have tried to adopt various new methods to carry out research on equipment selection and matching. [PERSON] [3] expounded the main basis and principle of the selection of mining and transport equipment based on the actual design of an open-pit mine. [PERSON] et al. [4] showed an evaluation index system for optimizing open-pit transport equipment from both qualitative and quantitative aspects. Taking the electric locomotive, truck transport and belt conveyor as decision-making objectives, they used the VIKOR (VIse Kriterijumski Optimizacioni Racun) method to optimize the open-pit transport equipment. [PERSON] et al. [5] combined the traditional open-pit mine equipment selection method with fuzzy hierarchical analysis, and comprehensively evaluated the advantages and disadvantages of equipment combinations through efficiency factors, economic factors and maintenance factors. [PERSON] et al. [6] analyzed equipment selection and equipment matching, and worked out the open-pit mining equipment selection scheme. [PERSON] et al. [7] analyzed the influence of the selection of the two key factors (truck-shovel ratio and bucket-to-capacity ratio) for matching the truck-shovel in an open-pit mine, and concluded that a reasonable truck-shovel ratio can reduce the owed and waiting time, and that a reasonable bucket-to-capacity ratio should comprehensively consider the carriage volume, the rated load of trucks and the properties of loaded materials. [PERSON] [8] comprehensively used a genetic algorithm and a reliability analysis methods to study the three aspects of shovel selection, truck number matching and truck-shovel system configuration in the truck-shovel system.