A discrete event simulation model for airport runway operations optimisation: A case study of Cairo International Airport
Bibliographic record
Abstract
Runways are the major crucial infrastructure for all airports around the world. The efficient handling of runway operations is the key to ensuring that airports are running smoothly with minimal delays and reduced queuing time on both approaches and departures. Many factors affect the efficiency of runway operations, such as the aircraft wake separation, runway system configuration, the number of runways and the fleet mix. This paper aims to address how to maximise runways operations by using the discrete event simulation model (DESM) at Cairo International Airport (HECA). The study has applied the model to evaluate the operational performance of an airport with three parallel runway setups to compare the result of the DESM with the actual runway performance in different operational scenarios. As HECA has provided its results from historical operations, the proposed scenarios will help to set the new benchmark to explore the opportunities for runways performance improvements at the airport.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".