Optimization Model for Sustainable End-of-Life Vehicle Processing and Recycling
Bibliographic record
Abstract
The aim of this paper is to provide a mathematical programming model for sustainable end-of-life vehicle processing and recycling. Environmental benefits and resource efficiency are achieved through the incorporation of a processing and recycling network that is based on industrial symbiosis whereby waste materials are converted into positive environmental externalities aimed at decreasing pollution and reducing the need for raw materials. A mixed-integer programming model for optimizing the exchange of material flows in the network is developed and applied on a real case study. The model selects the components that maximize reusable/recyclable material output while minimizing network costs. In addition, GHG emissions are calculated to assess the environmental benefits of the network. The model finds the optimal processing routes while maximizing the yield of the components of interest, maximizing profit, minimizing cost, or minimizing waste depending on which goals are chosen. The results are analyzed to provide insights about the network and the utility of the proposed methodology to improve sustainability of end-of-life vehicle recycling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".