Research on Optimization of Unmanned Vehicle Scheduling in Phosphate Mine Tunnels

In order to improve the transportation efficiency of unmanned underground phosphate ore transport vehicles, ensure safety, and control costs, this paper constructs a vehicle scheduling optimization model based on an improved genetic algorithm. First, the multi-working face is grouped and sequence-optimized according to the actual process flow. A binary real number encoding is used to describe the grouping, sequence, and vehicle configuration on the chromosome, thereby improving transportation efficiency. Secondly, combined with the UWB positioning system to obtain real-time vehicle location, a meeting strategy is designed to improve the safety and efficiency of vehicle encounters in the tunnels, with the meeting time accurately calculated in the fitness function. Finally, experimental verification shows that the optimized scheme significantly reduces transportation costs and improves efficiency.