A fuzzy ANP-based model for sustainable aircraft end-of-life planning: a case study of a mid-size regional airliner decommissioning
Bibliographic record
Abstract
The rising number of retired aircraft worldwide poses major economic, environmental, and social challenges. Selecting an optimal end-of-life (EoL) strategy is complex due to varying regional and situational factors. This study focuses on the disassembly of a mid-size passenger jet in Canada and highlights the importance of involving all key stakeholders - manufacturers, disassemblers, and post-disassembly partners - in the decision-making process. A fuzzy-based analytical network process (ANP) model is developed to support multi-criteria decision making (MCDM), addressing uncertainties and vagueness in stakeholder preferences. The model integrates sustainability criteria - environmental, economic, social, and technical - to evaluate potential EoL strategies. The application of this model in a real-world case demonstrates its ability to guide stakeholders toward selecting the most sustainable and feasible alternative. By balancing diverse objectives and constraints, this approach promotes effective collaboration and informed choices in the aircraft EoL process, ultimately benefiting the aviation industry, the environment, and broader society.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".