Improving the Performance of Urban Waste Management Systems in the Context of a Closed-Loop Supply Chain
Bibliographic record
Abstract
The saving of non-renewable energies, as well as the reduction of emissions into the environment, are two crucial objectives of industrial production. The recovery of post-consumer products associated with the use of end-of-life products is part of a context of optimization of these objectives. This recovery maximizes the use of resources from end-of-life products in a circular logic while recording the environmental footprint. This study considers a recycling strategy adapted to the need and urgency to reduce greenhouse gas emissions caused by global warming. The proposed model aims to optimize the profits of the circular manufacturing strategies while minimizing operational costs (collection, sorting, recycling), transport, GHG emissions and recycling. In this paper, a compromise between the gains of CM and the costs associated with it was studied. The robustness of the designed model was tested using a case study based on real-world scenarios. A sensitivity analysis was carried out to study the impact of the emission cost on the overall objective, considering the two options currently offered to industries. The obtained results support companies to take the ecological aspect into account and integrate sustainable development into their strategic axes for their logistics supply chains.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".