A rolling horizon solution approach for the airline crew pairing problem
Why this work is in the frame
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Bibliographic record
Abstract
The crew pairing problem (CPP) is one step of the airline crew scheduling process. The CPP consists of determining a minimum cost set of feasible pairings such that each flight is covered exactly once and side constraints are satisfied. In the industry, this problem has been traditionally solved by a heuristic three-phase (TP) approach that solves sequentially a daily, a weekly, and a monthly problem. The contribution of this paper is to show that the traditional approach is less efficient to solve the crew pairing problem when the flights schedule is not regular. In fact, we show that to obtain better quality solutions in less computational time it is better to skip the first two phases and directly solve the monthly problem using a rolling horizon (RH) approach based on column generation method. All experiments are tested on real data provided by a major airline company.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it