Unmanned mining fleet Management: A Multi-Objective framework integrating deep reinforcement learning and Internet of Things

Optimizing short-term production scheduling in open-pit mines using mining fleets is a complex yet essential task with a significant impact on productivity and cost reduction. This study addresses the growing need for intelligent fleet management systems to maximize the utilization of unmanned mining fleets for efficient production scheduling. A multi-objective production scheduling framework was developed, incorporating deep reinforcement learning and the Internet of Things (IoT) for real-time fleet management. The proposed model focuses on minimizing fleet idle time and transportation costs while maximizing total production through parallel control of multiple shovels and mining trucks. IoT-based travel time estimation was integrated to enhance fleet coordination and improve scheduling accuracy. The model was evaluated using both hypothetical (6 loading points, 2 unloading points, 6 shovels, and 40 trucks) and real-world cases (17 loading points, 3 unloading points, 17 shovels, and 117 trucks) from the Sarcheshmeh Copper Mine in Iran. The DQN-IoT model achieved a 19.2% reduction in truck idle time, outperforming Particle Swarm Optimization (PSO) (12.7%) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) (9.2%). Fleet utilization improved by 4.4%, compared to 2.9% (PSO) and 2.1% (NSGA-II). Operational costs were reduced by 5.5%, surpassing the savings of PSO (1.8%) and NSGA-II (1.2%). These results highlight the superiority of the proposed model and the practical benefits of integrating DQN and IoT in real-time fleet scheduling.