Reinforcement Learning-Driven Negotiation in a Multi-Agent System for Truck Dispatching in Open-Pit Mining

Material handling is an important process in open-pit mining, where trucks transport material extracted by shovels to different destinations within the mine. The decision regarding the next destination of a truck strongly influences operational efficiency. In current mining operations, this decision is typically handled by centralized dispatching systems based on predefined criteria. However, such approaches often struggle to adapt to dynamic operating conditions and rely on a central control unit, which may reduce flexibility and robustness. This paper proposes a decentralized multi-agent system for truck dispatching with reinforcement learning (MAS-TDRL). In the proposed approach, autonomous agents representing trucks, shovels, and unloading points cooperate through a negotiation mechanism based on an enhanced Contract Net Protocol to generate operational schedules. Reinforcement learning is integrated into the decision-making process of truck agents, allowing them to learn from previous negotiations and improve their participation over time. The proposed system is evaluated through simulation using scenarios based on real data from an open-pit copper mine in Chile. The results show that incorporating reinforcement learning increases the material transported per hour by approximately 18–29% compared to a multi-agent system without learning, while maintaining computation times below 10 min even in the largest scenario, which remains compatible with operational decision-making in open-pit mining contexts.