Integrating short-term stochastic production planning updating with mining fleet management in industrial mining complexes: an actor-critic reinforcement learning approach

Abstract

Short-term production planning in industrial mining complexes involves defining daily, weekly or monthly decisions that aim to achieve production targets established by long-term planning. Operational requirements must be considered when defining fleet allocation and production scheduling decisions. Thus, this paper presents an actor-critic reinforcement learning (RL) method to make mining equipment allocation and production scheduling decisions that maximize the profitability of a mining operation. Two RL agents are proposed. The first agent allocates shovels to mining fronts by considering some operational requirements. The second agent defines the processing destination and the number of trucks required for transportation. A simulator of mining complex operations is proposed to forecast the material flow from the mining fronts to the destinations. This simulator provides new states and rewards to the RL agents, so shovel allocation and production scheduling decisions can be improved. Additionally, as the mining complex operates, sensors collect ore quality data, which are used to update the uncertainty associated with the orebody models. The improvement in material supply characterization allows the RL agents to make more informed decisions. A case study applied at a copper mining complex highlights the method’s ability to make informed decisions while collecting new data. The results show a 47% improvement in cash flow by adapting the shovel and truck allocation and material destination compared to a base case with predefined fleet assignments.