Four-Dimensional Aircraft En Route Optimization Algorithm Using the Artificial Bee Colony
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fuel burn releases polluting particles to the atmosphere. Aeronautical operations have been estimated as being responsible for 2% of the total amount of carbon dioxide liberated to the atmosphere each year. Fuel is also one of the major expenses for airlines. Reducing the amount of fuel required to power flights brings benefits to both the environmental and economic aspects of the aeronautical industry. This paper aims to develop a new optimization algorithm that computes fuel-efficient aircraft reference trajectories inspired by the artificial bee’s colony and based on a numerical performance model. The flight trajectory is optimized in terms of speeds, altitudes, and geographical positions, while respecting the required time of arrival constraint. The optimal trajectory is composed of waypoints placed in each of the available dimensions (coordinates, altitudes, and speeds). Winds and temperatures are taken into account. These trajectories will be improved by taking all of the dimensions into consideration simultaneously, instead of improving them one after the other. Results have shown that, when flying under the free-flight concept and fulfilling the required time of arrival constraint, the algorithm saved around 5% of the fuel burn with respect to as-flown flights.
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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 | 0.001 | 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 | 0.002 |
| 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 it