Divide and Conquer Optimization for Closed Loop Supply Chains
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".