Short-term planning typically involves optimising different mining complex components individually, preventing unlocking value from intercorrelated activities. This paper proposes an integrated framework that defines diglines and shovel allocation decisions to improve the operation’s financial performance. This involves a clustering approach, defining diglines and material destination; a simulator of the mining complex operations, forecasting the material flow from benches to processors; and an actor-critic reinforcement learning approach, assigning shovels to mining areas given operating requirements. A case study at a copper mining complex shows 27% cash flow improvements compared to a non-adaptive baseline approach provided by recent advances in the field.