Four-Dimensional Aircraft En Route Optimization Algorithm Using the Artificial Bee Colony
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".