Coupled Optimization of Aircraft Families and Fleet Allocation for Multiple Markets
Bibliographic record
Abstract
The requirements for new commercial aircraft can differ significantly for different markets and operators. The economic and environmental sustainability of commercial aviation requires not only the design of efficient new aircraft but also consideration of the operations of these aircraft. This can be achieved by coupling the design optimization of multiple aircraft families with the simultaneous allocation of these aircraft in multiple markets. Including operational allocation of aircraft in the design stage can reduce operational inefficiencies, whereas the design of aircraft families aims at reducing costs through the use of common components and providing increased flexibility for different markets. To investigate the tradeoffs involved in designing efficient, environmentally sustainable aircraft, a coupled design optimization of two aircraft families involving uncertainties in passenger demand over multiple years of operations was conducted. The results obtained show that the coupled design of aircraft families with the allocation of these aircraft to two distinct markets can significantly reduce fuel burn, as well as operating and acquisition costs, when compared to existing aircraft. The optimized aircraft also provide higher operational flexibility, with respect to variations in passenger demand, and improved performance when compared against aircraft optimized individually, taking into consideration routes being flown but decoupled from fleet allocation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".