Multi-Criteria Decision-Making Approach with Interval-Valued Intuitionistic Fuzzy Assessment for Green Supplier Evaluation and Selection
Bibliographic record
Abstract
Becoming climate positive is the objective businesses, which have recently started to focus on and research about to curb the ever-increasing climate challenges while being profitable efficiently. In the pursuit of integrating environmental aspects into conventional supply chains, the concepts of green supply chain and supplier selection have emerged that become increasingly complex due to current economic globalization. In this research, we have proposed a green supplier selection methodology based on a comprehensive set of criteria and interval-valued intuitionistic fuzzy assessment of alternatives for realistic decisions. The three phases of the methodology constitute pertinent criteria selection incorporating environmental and social aspect, the assessment of alternatives in an interval-valued intuitionistic fuzzy environment through an integrated multi- criteria decision-making approach comprising best-worst method (BWM) and technique for order preference by similarity to ideal solution (TOPSIS) with cosine similarity measure and finally the sensitivity analysis. We have supported the proposed methodology by illustrative example considering a real-world scenario.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".