Coupled Optimization of Aircraft Design and Fleet Allocation with Uncertain Passenger Demand
Bibliographic record
Abstract
The design of future aircraft takes into great consideration the current market requirements and future needs of potential operators. In spite of such efforts, the design of new aircraft and the assignment of these aircraft to a specific route in the operator’s network are loosely coupled. This results in many aircraft operating on routes with significantly lower ranges than the design range of the aircraft and potential operational inefficiencies. In addition, increasing air traffic demand and the resulting climate impact of aircraft are of growing public concern. Reductions in future climate impact from air transportation require not only the design of efficient new individual aircraft, but also consideration of the operations of these aircraft during the design stage. This paper describes a multidisciplinary design optimization approach for the coupled optimization of aircraft and the simultaneous allocation of these aircraft types to routes in an operator’s network with uncertain passenger demand. The uncertainty characteristics of trip–demand for the routes in the network are considered to ensure the efficient utilization of these aircraft in the given network and to explore the effects of future trends in commercial aviation in terms of environmental considerations. The uncertainty characteristics of passenger demand are included in the allocation optimization problem through the use of discrete time simulation of the operations. This paper explores the potential benefits and tradeoffs in terms of environmental and cost considerations of coupling the design of new aircraft with their respective use by operators.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".