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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".