Bridging gaps in intelligent truck dispatching: An underexplored PPO-based model with expanded feature integration within open pit mines
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".