The mining industry faces a substantial increase in data generation, making real-time knowledge acquisition challenging. Mining data from day-to-day operations from monitoring devices and sensors include the location of a truck or shovel, processing rate, and quantity of material mined. The complexity of decision-making in mining has grown due to intricate mineral deposits and the need for improved productivity.

The focus of this thesis is to develop a decision support framework referred to as smart mining architecture (SMA), which leverages the power of simulation and reinforcement learning for understanding mining system interactions and enhancing mining operational performance. The mining operation and activities of mining equipment are simulated and modelled using a discrete event simulation (DES) and an agent-based model (ABM) respectively. The primary objective of this research is to develop an intelligent supervisory agent capable of making smart decisions such as block sequencing, truck-shovel dispatching and equipment maintenance and repairs towards improved operational performance. A reinforcement learning deep quality network (DQN) algorithm is used to develop and train the agent. The aim of the agent is to learn a policy that maximizes the expected return by interacting in real-time with its environment taking smart actions to improve the learning policy.

The developed intelligent supervisory agent (ISA) was successfully implemented for a bauxite mine. The model was used to perform block sequencing, truck-shovel dispatching and equipment maintenance and repairs in the SMA. Experimental cases with integrated ISA were compared with experimental cases without the ISA. The output results with ISA maintained consistent production levels in the presence of mining operational uncertainties. Additionally, in the final experiment involving the DES, ABM and ISA, there was 3% increase in total cashflow from the smart management of the short term mine plan and mining equipment deployment as compared to experiments without the ISA. With the introduction of the ISA, fewer workers are required to run the mining operation resulting in financial savings. Fewer workers also mean less interaction between people and equipment leading to improvement in health and safety records and occupational health challenges.