Arrival Sequencing and Scheduling using an Evolutionary Approach in a 4D Environment
Bibliographic record
Abstract
The aim of this article is to use an Evolutionary Algorithm (EA) to solve the Aircraft Landing Problem (ALP) in an Air Traffic Flow Management (ATFM) environment. The ALP addresses the function of generating optimal or near-optimal landing sequences and time intervals between arrivals to provide runway capacity increase and reduce air delay. Problems of the ALP type in a dynamic environment such as Air Traffic Control (ATC) are considered Non-Polynomial (NP) complete. We simulated three different models. In the first model, the algorithm was applied when there was a schedule conflict between aircraft and separation measures where used to ensure safety. On the second and third models,we scheduled the flights in hourly batches. In the third model, a Maximum Constrained Shift (MCS) restriction was introduced to simulate more realistic conditions. To test the effectiveness of our study, we used actual data from Guarulhos International Airport. Results showed a capacity gain of 12 aircraft and a delay decrease of five percent when compared to the airport current sequencing operations. Introducing this technique represents a shift from the current arrival sequence model to a Trajectory-Based Operations (TBO) model, balancing air traffic demand with airspace capacity to ensure the most efficient use of the airspace system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".