Improving Air Crew Rostering by Considering Crew Preferences in the Crew Pairing Problem
Bibliographic record
Abstract
A common strategy used by airlines to improve employee satisfaction is to create schedules that take into account crew preferences such as preferred legs or desired off-periods. Air crew scheduling usually involves two steps: the crew pairing problem (CPP) and the crew rostering problem (CRP). A pairing is a sequence of legs and deadheads separated by connections and rest periods that starts and ends at the same crew base and can legally be operated by a crew member. The CPP generates a set of pairings that covers every leg of a given schedule exactly once at a minimum cost. The CRP uses these pairings to create rosters composed of personalized schedules, with the goal of granting as many crew preferences as possible. A downside of this two-step approach is that the CPP does not take the crew preferences into account, resulting in CPP solutions that are often ill suited for the CRP. In this paper, we propose a new variant of the CPP, called the CPP with complex features (CPPCF), that considers the crew preferences in order to create pairings that are better suited for the CRP. Specifically, we identify six pairing features related to crew preferences that are beneficial for the CRP, and the objective function of the CPPCF rewards pairings that contain these features. We solve the CPPCF using a column generation algorithm in which new pairings are generated by solving subproblems consisting of constrained shortest path problems. For this purpose, we introduce a new type of path resources designed to handle complex features, and we adapt the dominance rules accordingly. We test the proposed CPPCF approach on seven real-world instances from a major North American airline and show that a combination of these features significantly improves the solutions of the CRP.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".