Research on optimization of flight crew scheduling considering pilot fatigue
Bibliographic record
Abstract
Safety is a top concern for the civil aviation industry, and the risk of safety accidents will increase due to pilot fatigue. To ensure the safety of civil aviation, this paper proposes a method to solve the crew scheduling problem considering pilot fatigue. In order to reflect individual differences and fatigue levels of pilots, an improved three-stage alertness calculation model is first proposed based on subjective and objective perspectives to represent pilots’ alertness levels and fatigue working duration quantitatively. Then, for the crew scheduling problem considering pilot fatigue, a mixed integer programming model is constructed to simultaneously achieve the optimization objectives of reducing the overall scheduling cost and crew fatigue working duration. Next, since the actual crew scheduling problem is large-scale, a solution algorithm based on a column generation framework is developed to improve the quality and efficiency of solving the large-scale crew scheduling problem. Furthermore, in the case study, we collected actual data from an airline company to validate the effectiveness of our proposed method. Finally, through multiple experimental comparisons and analyses, to balance the two optimization objectives mentioned above, it is more reasonable to handle pilot fatigue working duration with soft constraints. Sensitivity analysis reveals the variation rules of the crew cost and fatigue, providing some valuable managerial insights for the crew scheduling problem considering pilot fatigue.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".