Truck Fleet Dispatching Control in Open-Pit Mining Based on Reinforcement Learning and Discrete Event Simulation

Truck fleet dispatching plays a crucial role in reducing operational costs and fulfilling operational targets in open-pit mining operations and is subject to large uncertainty arising from the operating cycles of trucks and loading equipment. This paper proposes a reinforcement learning-based truck dispatching system. Reinforcement learning is a machine learning area that deals with learning an optimal sequential decision-making strategy in an uncertain environment. An open-pit load-and-haul simulation is developed that also captures the truck movements and interactions in the shared road network, and a Neural Network is successfully trained to suggest truck dispatching decisions in a real-time manner.