Improving the Objective Function of the Fleet Assignment Problem
Bibliographic record
Abstract
Most fleet assignment problem (FAP) formulations use a leg-based estimation of revenue loss to derive the passenger revenue component of their objective function. This neglects the leg interdependency of revenues, caused by multileg itineraries. We tackle this problem by modifying the objective function using information provided by a passenger flow model devised by two of the authors. It models spill and recapture between itineraries, accounts for the leg interdependency of revenues and does not control passenger flow to the airline company's advantage. We iteratively improve the FAP's objective function by alternately generating fleet assignments and analyzing them with a modified version of the passenger flow model. We have tested this process on a large-scale network made up of Air Canada data with various demand levels and distributions. Most of the profit improvement occurs in the first few iterations, and the objective function adjustment takes on average less than half the FAP resolution time.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".