Cost-Effective Flight Sequencing and Gate Assignment Considering Transfer Time under Pandemic Conditions
Bibliographic record
Abstract
An efficient hub-and-spoke network (HSN) can reduce operating costs and passenger delays at hubs through proper sequencing of flights and assignment of gates. Typically, batches of flights are scheduled to arrive within brief time windows. By considering aircraft sizes/loads and locations of the available gates, there is a considerable potential for reducing the total cost. During pandemic conditions, passenger transfer time and aircraft dwell time significantly increased because of outbreak controls (e.g., COVID-19 and its variant). In addition to walking time between connecting flights, there is a significant delay for the airport staff to validate passengers’ proper travel documents. The objective of this study is to minimize the total cost by optimizing the flight sequencing and gate assignment and by considering the realistic transfer delay under pandemic situations. A genetic algorithm with an elite selection strategy is developed to search for the optimal solution, which significantly reduces the total cost by 25% compared with that under existing operations, and the relations between optimized solutions and various model parameters are explored.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".