Optimisation Method for Intelligent Scheduling System of Coal Mine Auxiliary Transportation via Multi-Objective Reinforcement Learning

Key Points

• The central aim is to develop an intelligent scheduling system to enhance coal mine auxiliary transportation efficiency using multi-objective reinforcement learning.
• Utilized multi-objective reinforcement learning algorithms for scheduling tasks
• Implemented intelligent systems to improve transportation efficiency
• Conducted simulations to compare traditional versus optimized scheduling methods
• Achieved a 20% reduction in transportation delays compared to traditional methods
• Increased overall system efficiency by 15%
• Observed improved resource allocation in coal mine operations