Journal of Engineering Research

Transportation plays a crucial role as a link in the production process of smart mines, with transportation costs accounting for 50% to 65% of the total cost of open-pit mines and fuel consumption accounting for 60% to 72% of the total energy consumption. The transportation effect depends on the quality of the main carrier of transportation - trucks, such as the reliability and intelligence of operation, and on the scheduling algorithm of mining truck operations. Dedicated to improving the scheduling efficiency of mining truck operations and helping to achieve intelligent and sustainable mining, this article mainly conducts research on scheduling algorithms for mining truck operations. In response to the lack of research on real-time scheduling algorithm models for mining truck operations, truck scheduling is divided into heavy truck scheduling and empty truck scheduling, mainly considering factors that affect mining production such as traffic flow planning, transportation costs, and truck operating efficiency. In order to verify the effectiveness of the scheduling model, MATLAB software simulation is used to solve the scheduling results within a shift using genetic algorithm and the maximum truck method Compare the earliest loading and unloading method with the vehicle flow planning method to evaluate the superiority and inferiority of scheduling algorithms. Comparing the performance indicators of various scheduling schemes, considering transportation costs in the objective function of this articles scheduling scheme, compared to the maximum truck method and traffic flow planning method, the transportation cost per ton of minerals is effectively reduced, and it performs outstandingly in the coupling degree of traffic flow planning, transportation capacity.

Open pit mines; Fuel trucks; Real time scheduling; Genetic algorithm