Bridging gaps in intelligent truck dispatching: An underexplored PPO-based model with expanded feature integration within open pit mines

Abstract

The mining industry is increasingly adopting intelligent systems to address challenges such as declining ore grades, rising operational costs and sustainability demands. This study presents a Proximal Policy Optimisation (PPO)-based truck dispatching model designed to enhance operational efficiency in open-pit mining. Addressing two key research gaps – limited integration of dispatching features and underutilisation of advanced reinforcement learning (RL) algorithms – the proposed model incorporates 19 critical features and is evaluated against the conventional Fixed Schedule (FS) method. A discrete event simulation environment was developed to emulate an open pit case study with heterogeneous trucks and shovels. The PPO model demonstrated convergence within 3.5 h and outperformed the FS baseline across multiple key performance indicators, including a 5.7% increase in total production, 4.2% improvement in plant delivery, and 13.2% higher truck utilisation. Compared to widely used RL algorithms in this domain, the PPO approach achieved faster convergence despite handling a more complex feature set. These findings highlight the potential of PPO as a robust and scalable solution for intelligent dispatching, offering practical benefits for Mining 4.0 initiatives.