Multi-Criteria Decision Making Models for Sustainable and Green Supply Chain Management Based on Fuzzy Approach
Bibliographic record
Abstract
Understanding different aspects of sustainability, supply chain management (SCM), and decision making policies and relating them to performance measurement have been increasingly investigated in the last decade. In contrast to traditional SCM, which typically focuses on economic and financial business performance, sustainable SCM (SSCM) is characterized by explicit integration of environmental or social objectives which extend the economic dimension. For evaluating the sustainability of SCM as well as its greenness, we have to consider many and different index and criteria. One of the best tools for assessing the SSCM and GSCM is multicriteria decision making (MCDM) techniques. Many studies have been conducted in this area. Moreover, there are many uncertainty factors which may reduce the accuracy of MCDM result. Actually, Uncertainty is always a worsening factor in any decision support models, and dilutes the planned objectives of such models. For decreasing this uncertainty, fuzzy logic has been combined with MCDM approach. In fact, the main purpose of this chapter is considering the recent studies in area of SSCM and GSCM regarding to applications of fuzzy MCDM techniques. At the end of this chapter, based on out investigations in applications of fuzzy MCDM in SSCM and GSCM and regarding to research gaps, some suggestions for future studies have been proposed.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".