Optimal Fuel Selection Using a Hybrid VIKOR-TOPSIS Approach: A Comprehensive Analysis of Environmental Regulations and Road Transport Pollutant Emissions
Bibliographic record
Abstract
The increasing global demand for fuel, driven by population growth and transportation expansion, has resulted in significant environmental and health challenges due to emissions from fossil fuels, particularly diesel.This study presents a hybrid Multi-Criteria Decision-Making (MCDM) approach that combines ViseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Criteria Importance Through Inter-Criteria Correlation (CRITIC) methods to evaluate and rank six fuel alternatives based on their performance and environmental impact.Key performance indicators, including brake thermal efficiency, exhaust gas temperature, mass flow rate, and air-fuel ratio, were analyzed to assess their effect on engine efficiency and pollutant emissions.The results showed that diesel with gasoline fumigation (D+GF) ranked first in most scenarios, including RC1 and RC5, while B20 (80% diesel, 20% biodiesel) ranked second in all cases except RC6, where it ranked third.B20 was found to be more suitable for highpollution areas due to its lower emissions.The study demonstrated that D+GF improved engine efficiency by 10% compared to pure diesel and reduced emissions by up to 15% under certain conditions.This research highlights the importance of adopting cleaner fuels and recommends integrating advanced MCDM techniques, such as fuzzy MCDM and machine learning-aided MCDM, for future assessments.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".