A multi-period performance analysis of airlines: A game-SBM-NDEA and Malmquist Index approach
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".