Abstract
In the context of increasingly stringent fuel consumption regulations, new energy trucks have become one of the equipment for mining transportation. However, the batteries of new energy trucks consume a significant amount of energy, necessitating rational planning and scheduling to reduce energy consumption. This paper explores a mine transportation scheduling solution based on a hybrid genetic algorithm, with special consideration given to the energy recovery characteristics of electric trucks. Through an analysis of battery energy consumption and recovery during truck operation, and a comprehensive consideration of factors such as transportation time between various mine nodes, road slope and length, as well as node mining and queuing time, the constraints of transportation route selection and vehicle scheduling were examined. The study establishes a multi-objective optimization model that seeks a reasonable structure while taking into account maximizing transportation volume and minimizing costs.The Dijkstra algorithm, combined with the breadth-first search algorithm, is employed to calculate suitable paths. Subsequently, the non-dominated sorting genetic algorithm is utilized for vehicle task allocation through a unique genetic encoding method. Mathematical methods, including entropy weighting, are incorporated to address the multi-objective weighting problem of automatic truck scheduling. To enhance the realism of the solution, the model also accounts for time-varying changes in road congestion and road bifurcation points traversed by truck routes. Finally, simulation experiments validate that the established energy optimization model has a positive impact on mine operations by increasing mining transportation volume, reducing costs, and minimizing wasted time. Additionally, the proposed method exhibits robustness and practicality, demonstrating certain fault adjustment capabilities.