A multi-period performance analysis of airlines: A game-SBM-NDEA and Malmquist Index approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The airline industry is one of the major industries having a significant role in the economic development of a country, on both domestic and international sides. Hence, it is important to have the airlines performing efficiently, as much as possible. To this end, it seems necessary to continuously evaluate the performance of the airlines to find any possible chance to improve their performance. In this study, by combing the ideas of the Egalitarian Bargaining game theory, Network Data Envelopment Analysis (NDEA), and Slack-Based Measure (SBM), a new game-SBM-NDEA model has been proposed to evaluate the performance of the Decision Making Units (DMUs) with a series network structure. In addition to handling the between-stages conflict in the network structures, the proposed model can provide more reliable efficiency scores when the number of the DMUs is not large enough compared to the number of considered inputs and outputs. The developed model and Malmquist Index have been applied to analyze the performance of Iranian domestic airlines over an 8-years period from 2013 to 2020, as a real-world case study. The obtained results for overall efficiency scores, operational efficiency, service efficiency, slack/surplus values for all inputs and outputs, and efficiency changes over time have been comprehensively analyzed in order to obtain the deficiencies of each airline and find possible solutions to improve their performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.028 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it