Comparative analysis of airline financial and operational performances: A fuzzy AHP and TOPSIS integrated approach
Bibliographic record
Abstract
Already faced with tight competition and low profit margins, the airline industry is going through major changes in the wake of the current pandemic resulting in travel restrictions and slump demands, prompting airlines to curtail services and investments in every aspect of business. To that end, developing a comprehensive method of improving airline performance measures is crucial. However, this type of problem is complex to solve due to a large number of factors, requiring a systematic approach. It entails taking into account a multitude of conflicting, or sometimes interrelated criteria, hence becoming an inherently multiple criteria decision making problem. This study is aimed to assess the competitiveness of airlines and evaluate their financial and operational performances in relation to such criteria. We test FAHP, TOPSIS, and a hybrid method of combining FAHP and TOPSIS methods. In particular, regarding the hybrid method, FAHP is employed to determine the influential weights of criteria that are utilized in TOPSIS for preference values among alternatives. We demonstrate the applicability of the proposed methods to solving a MCDM problem of airline performance assessments using real data sets. Further, this study focuses on examining the relationship between financial and operational performance criteria, as well as gleaning insights for airlines to build an evaluation system that would aid in understanding their strength and weakness in the performance metrics. The computational experiment results of our hybrid FAHP-TOPSIS model support the efficacy of incorporating fuzzy values concerning influential weight criteria. By judiciously distributing criteria weights that are specific to the airline industry, our proposed model captures preference scores reflective of industry-related and concurrent measures. This modeling framework can help airlines better evaluate the systematic influential relation structure among criteria in critical financial and operational dimensions.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".