The hybrid DHP method for evaluation, ranking and selection of green suppliers in the supply chain
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
This paper presents a hybrid method called the DHP method, which is a combination of data envelopment analysis and hierarchical analysis process methods. Evaluation, ranking, and selection of green suppliers in the supply chain are important to reduce costs and increase profits, efficiency, and performance of the supply chain. The purpose of this paper is to evaluate and select the best green suppliers of wind turbine equipment using the proposed DHP approach. In fact, it deals with the process of hierarchical analysis of supplier performance and uses a cross-performance matrix instead of a weight matrix. Finally, in order to validate the proposed DHP method, a similar method is used to solve the problem of green supplier selection, and the results show the superiority of the proposed method in supplier selection. The most important advantage of the proposed approach is the simultaneous consideration of suppliers' performance and their evaluation according to the existing criteria.
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.
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 | 0.112 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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