Optimization of Time Slots for the Air-Traffic Management
Bibliographic record
Abstract
The allocation of airport time slots depends on the configuration of the airport, particularly on that of the runways. Thus, in order to allocate the slots optimally in an airport platform, we proposed two optimization models in this article.The first model maximizes the airlines companies demands in the periods by taking into account the characteristics of the airport. This model assigns the flights' demands. It allows determining the number of demands which we can satisfy in a given period of amplitude of one hour. It also helps to incorporate dynamically the unmet demand of j period to the j+1 period.The second model aims to schedule the confirmed requests by the first assigning model. We are interested in the optimal repartition of the confirmed requests, while minimizing the flights delays. These models are used to optimize the air-traffic management of the Diass airport. Therefore, we have developed and implemented two algorithms for the resolution of these two models. The ressults of experimentations in Cplex show that our algorithms are efficient comparing to those obtained by the reference works existing in this field. The data used are those of the International Blaise Diagne Airport (AIBD).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".