A Heuristic and Exact Method: Integrated Aircraft Routing and Crew Pairing Problem
Bibliographic record
Abstract
<p>In airline operations planning, there are four problems which are schedule design, fleet assignment, aircraft routing and crew pairing problem. Those problems are sequentially and interdependent. Aircraft routing and crew pairing problem are hard to solve and normally crew pairing problem dependent to the aircraft routing problem which gives the suboptimal solutions. As minimizing the costs is important in the airline system, so in order to tackle suboptimal solutions, aircraft routing problem and crew pairing problem are being integrated in one model. For solving the integrated model, the feasible aircraft routes and crew pairs are required. Because of that, a method is being proposed in this work for generating the feasible aircraft routes and crew pairs which is the constructive heuristic method. By using the generic aircraft routes and crew pairs, the integrated model then being solve by two approaches. The first approach is the exact method called the integer linear programming (ILP) while the second approach is from the heuristic method called particle swarm optimization. Encouraging results are encountered by testing on four types of aircrafts for one week flight cycle from local flights in Malaysia.</p>
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".