Viable Closed-Loop Supply Chain Network with Considering Robustness and Risk as a Circular Economy
Bibliographic record
Abstract
Abstract The Viable Closed-loop Supply Chain Network (VCLSCND) is a new concept that integrates sustainability, resiliency, and agility in a circular economy. We suggest a new form of robust stochastic optimization for this problem by minimizing the weighted expected, maximum and Entropic Value at Risk (EVaR). We proposed this form to increase robustness against demand disruption and energy productivity. Finally, we located CLSC components and assigned flow in the automotive industry. The results show that the cost of VCLSCND is less than without viable and has -0.44% gap. By increasing the conservative coefficient and confidence level, decreasing the allowed maximum energy and increasing the scale of the main model, the cost function, time solution and energy consumption grow. We suggested applying the Fix-and-Optimize algorithm for producing upper bound of large-scale. As can be seen, the gap between this algorithm and the main problem for cost, energy and time solution is approximately 6.10%, -8.28%, and 75.01%.
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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".