Divide and Conquer Optimization for Closed Loop Supply Chains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Environmental concerns and government regulations have encouraged/forced real-world supply chains to take back used product for recycle. A smart supply chain should therefore leverage significantly on the returned “used products” to produce new products at nominal cost and time. In such closed loop supply chains, the crucial challenge is to synchronize the used products recycling system, the production facility and the new product distribution system in the presence of uncertain customer demands and used product returns. The main motivation of this study is to improve the performance of closed loop supply chains using a divide and conquer optimization scheme. In this paper, it is shown that the manner in which the closed loop supply chain is divided into various subsystems, the way in which interactions between the subsystems are handled and the optimization sequence adopted can help to improve the profitability of the supply chain with minimal computational load. Comparisons between the proposed method and traditional single objective optimization are made to illustrate the advantages of the divide and conquer approach to closed loop supply chain optimization.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it