On the design of ustainable, green supply chains
Bibliographic record
Abstract
Increasing regulatory legislations for carbon and waste management and the concerns on corporate social responsibility are driving forces behind sustainable supply chain network design which involves taking into account social, economic and environmental objectives at design time. While the social dimension is sometime harder to capture or quantify in mathematical terms, the emission trading schema (ETS) introduces a natural trade-off between the economic and the environmental dimensions. This article addresses the design of supply chains that are sensitive to the carbon market where carbon emissions (environmental dimension) and total logistics costs (economic dimension) are integrated in the design of the supply chain using a multi-objective mixed-integer linear programming model that is solved by goal programming. The approach is presented through an illustrative example in the steel industry where new legislation imposes regulatory carbon caps on emissions. The results show that this approach is viable and offers a good starting point for a comprehensive framework for sustainable supply chain network design that can be expanded to include social aspects relating to the community.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".