Optimizing a Closed-Loop Supply Chain for Electronic Waste Management
Bibliographic record
Abstract
This study explores the integration of reverse and forward supply chains into a Closed-Loop Supply Chain (CLSC) in the context of electronic waste network design and optimization in business and governance. It emphasizes the economic advantages and efficient management of Electronic Waste (E-waste), particularly in the context of End-Of-Life (EOL) products, as a response to the global E-waste crisis. This research introduces a novel mixed-integer linear programming model for an E-waste CLSC network, featuring hybrid manufacturing facilities and an objective function that maximizes the total profit to reduce disposal in landfills. The application of this model to a computer manufacturing network in Ontario, Canada is discussed. To this aim, the distances are calculated using Google Maps. The proposed model provides insights into the design and optimization implications of an electronic CLSC network. The proposed approach seeks to decrease E-waste by incorporating new and recycled components, with some recycled items sold in a secondary market. The findings offer a comprehensive understanding of product and part flows. The open facilities and the number of parts and products dispatched within each segment of the network are determined. This study concludes with a summary and recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".