Enhancing sustainability performance of a closed-loop supply chain for protein products using a fully fuzzy multi-objective optimisation
Why this work is in the frame
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Bibliographic record
Abstract
This study addresses the critical issue of uncertainty in optimising food supply chains, a key factor impacting food security, sustainability and waste reduction. We introduce a novel, fully fuzzy multi-objective optimisation model designed for a closed-loop food supply chain that explicitly considers real-world uncertainties in parameters and decision variables. Our model aims to maximise profit, product and distribution quality, and service level, while minimising environmental impacts through reduced greenhouse gas emissions and waste. Applying this model to a real-world case study in Iran, we demonstrate significant improvements over current practices, including a 6% increase in profit, a 2% improvement in service level, a 3% enhancement in quality, a 4.6% reduction in product return rates and a 5.8% decrease in greenhouse gas emissions.
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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 | 0.004 | 0.006 |
| 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 | 0.001 | 0.002 |
| 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