Improving productivity in mining operations: a deep reinforcement learning model for effective material supply and equipment management

This research study examines the impact of shifts and lunch breaks on mining operations, particularly focusing on delays in hauling equipment used to supply extracted material to crushers. These delays significantly reduce productivity, averaging below 80% during regular working hours and adversely affecting mine profitability. To address this issue, a Q-learning-based deep reinforcement learning model was developed, utilizing real-world data from mining operations. The model aimed to achieve a 90% coverage in material supply to the crushers. A simulation environment, closely resembling the physical mining setting, was created to test the trucks as agents. Various scenarios, including equipment selection, cycle time, queue times, and material types, were considered. Based on the results, a deep learning model was trained to maximize coverage by determining the optimal combination of trucks and crushers. The solution successfully achieved a 90% supply coverage during shift changes and lunch breaks, with average execution times of less than 1 ms, making it suitable for real-time applications. This research demonstrates the effectiveness of the proposed Q-learning deep reinforcement learning model in optimizing material supply and enhancing mining productivity. By addressing delays and improving operational efficiency, this model holds significant potential for improving profitability in mining operations.