Unmanned mining fleet Management: A Multi-Objective framework integrating deep reinforcement learning and Internet of Things
Bibliographic record
Abstract
Optimizing short-term production scheduling in open-pit mines using mining fleets is a complex yet essential task with a significant impact on productivity and cost reduction. This study addresses the growing need for intelligent fleet management systems to maximize the utilization of unmanned mining fleets for efficient production scheduling. A multi-objective production scheduling framework was developed, incorporating deep reinforcement learning and the Internet of Things (IoT) for real-time fleet management. The proposed model focuses on minimizing fleet idle time and transportation costs while maximizing total production through parallel control of multiple shovels and mining trucks. IoT-based travel time estimation was integrated to enhance fleet coordination and improve scheduling accuracy. The model was evaluated using both hypothetical (6 loading points, 2 unloading points, 6 shovels, and 40 trucks) and real-world cases (17 loading points, 3 unloading points, 17 shovels, and 117 trucks) from the Sarcheshmeh Copper Mine in Iran. The DQN-IoT model achieved a 19.2% reduction in truck idle time, outperforming Particle Swarm Optimization (PSO) (12.7%) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) (9.2%). Fleet utilization improved by 4.4%, compared to 2.9% (PSO) and 2.1% (NSGA-II). Operational costs were reduced by 5.5%, surpassing the savings of PSO (1.8%) and NSGA-II (1.2%). These results highlight the superiority of the proposed model and the practical benefits of integrating DQN and IoT in real-time fleet scheduling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".