An integrated architecture for fixed truck assignment optimization in open-pit mines: Synergistic optimization, hybrid modeling, and multi-objective optimization
Bibliographic record
Abstract
Fixed truck assignment (FTA) is a prevalent method of truck dispatching in open-pit mining. To improve equipment utilization and reduce energy consumption, this paper proposes an integrated architecture for optimizing FTA. The innovations of this architecture include three aspects: (1) The concept of synergistic optimization is introduced into FTA for the first time, with a proposed approach of “haul distance–transport grouping–fleet speed.” This approach establishes the relationship between equipment utilization, energy consumption, and their influencing factors. (2) A hybrid modeling method is designed, integrating Kernel Principal Component Analysis and Artificial Neural Network (KPCA-ANN) into the programming model to represent the NP-hard nature of the FTA optimization. (3) An adaptive reference point-based Nondominated Sorting Genetic Algorithm III (ARP-NSGA-III) is introduced, incorporating both letter and real-valued encoding to solve the 3 × b -objective ( ∀ b ∈ Z + ) optimization problem across various route topologies. Finally, the integrated architecture is evaluated using historical data from a case mine. The results show that the optimal FTA solution reduces the shovel idle time (SIT) by 679 h, truck waiting time (TWT) by 1.65 × 10 5 h, and truck fuel consumption (TFC) by 6.2 × 10 6 L. The reduction in fuel consumption (FC) is equivalent to reducing 1.65 × 10 4 tons of CO₂ emissions, accounting for 8.3 % of the total CO₂ emissions from haul activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".