Optimizing a Reverse Logistics System by Considering Quality of Returned Products
Bibliographic record
Abstract
Coordination is one of the critical issues in remanufacturing systems that can persuade supply chain parties to make optimal centralized decisions leading to higher profits. Accordingly, this paper aims to examine a reverse logistics system, including one manufacturer along with a collector who collects used products based on the consumers' willingness to return such products. Consumers’ willingness is dependent on the take-back price, which is adjusted based on various quality levels affecting the processing cost of the collected items. This study developed mathematical models under both decentralized and centralized scenarios. Besides, to align the interests of both members and better profit-sharing, a cost-sharing contract is implemented. According to the results, in the coordination model, the take-back price of the high-quality level is increased compared to the decentralized model while the take-back price of the low-quality level is decreased. Hence, it suggests collecting and repairing higher-quality products to achieve higher profits for the whole system. Besides, the paper provides valuable suggestions for managers to resolve the conflicts of interest among participants of reverse logistics systems in an efficient manner.
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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 | 0.003 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".