Transition to intelligent fleet management systems in open pit mines: A critical review on application of reinforcement-learning-based systems

Abstract

The mathematical methods developed so far for addressing truck dispatching problems in fleet management systems (FMSs) of open-pit mines fail to capture the autonomy and dynamicity demanded by Mining 4.0, having led to the popularity of reinforcement learning (RL) methods capable of capturing real-time operational changes. Nonetheless, this nascent field feels the absence of a comprehensive study to elicit the shortfalls of previous studies in favour of more mature future works. To fill the gap, the present study attempts to critically review previously published articles in RL-based mine FMSs through both developing a five-feature-class scale embedded with 29 widely used dispatching features and an insightful review of basics and trends in RL. Results show that 60% of those features were neglected in previous works and that the underlying algorithms have many potentials for improvement. This study also laid out future research directions, pertinent challenges and possible solutions.