Airport gate reassignments considering deterministic and stochastic flight departure/arrival times
Bibliographic record
Abstract
SUMMARY It is often the case in actual airport operations that flight departure/arrival information will vary with time. In practice, flight departure/arrival times closer to the time when the airport authority starts to plan the reassignments tend to be more certain; those further away tend to be more stochastic. These two types of flights can be called deterministic flights and stochastic flights, respectively. A deterministic flight has a certain departure/arrival time; while a stochastic flight will have a variety of stochastic departure/arrival times. In this study the aim is to develop a gate reassignment model (GRM) designed to consider both deterministic and stochastic flight departure/arrival times. A 0–1 integer programming technique is applied to formulate the GRM. In practice gate reassignments need to be handled repeatedly, so to make this possible the GRM is applied to a dynamic gate reassignment framework (DGRF). The theoretical effectiveness of the GRM applied to the DGRF is evaluated by the development of a lower bound solution. Numerical tests, related to the operations of an international Taiwan airport, show that the proposed GRM and DGRF perform well. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".