Evaluating competitiveness using fuzzy analytic hierarchy process—A case study of Chinese airlines
Bibliographic record
Abstract
SUMMARY With the development of a national market economy, the Chinese aviation industry is now confronted with international competition. Therefore, it is necessary to research the competitive status of Chinese national aviation, as well as advice on how to enhance the competitiveness of the Chinese aviation industry. The main objective of this paper is to propose FAHP as an effective solution for resolving the uncertainty and imprecision in the evaluation of airlines' competitiveness. In this paper, we review the research of industrial international aviation competitiveness at both home and abroad, discuss a theoretical framework for the study of aviation competitiveness, establish an index system with five first‐order indicators and 17 second‐order indicators, set up a Chinese aviation competitiveness model based on simple fuzzy numbers from the fuzzy analytic hierarchy process, and evaluate the competitiveness of five major Chinese airlines. The results showed that this model and these indicators are scientific and practical, with a wide range of application prospects for the purpose of improving and increasing Chinese airline competitiveness in the international market. The effective approach presented in this paper is especially applicable when subjective judgments on performance ratings and attribute weights are not accessible or reliable, or when suitable decision makers are not available. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".