Dynamic Scheduling of Airport Flights with Graph Neural Networks
Bibliographic record
Abstract
Dynamic flight scheduling is critical for ensuring efficient airport operations, punctuality, and resource optimization in response to unpredictable factors such as weather changes, air traffic control restrictions, and cascading delays. Current flight scheduling approaches, often relying on rule-based systems or simplistic mathematical models, face limitations in their responsiveness to sudden disruptions, lack of global optimization, and inability to account for complex spatiotemporal relationships and dynamic factors in real time. To address these challenges, we propose a novel dynamic flight scheduling method based on Dynamic Graph Neural Networks (DGNN). This method models the interrelations among flights, resources, and time, and integrates DGNN into a multi-objective integer programming framework to minimize remote stand assignments, taxiing distances, and schedule adjustments. Additionally, DGNN features are used to enhance the NSGA-II algorithm, improving initialization, clustering-based optimization, and fitness evaluation. Validation on real-world datasets demonstrates that the proposed approach achieves significant improvements in operational efficiency compared to existing methods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".