Pricing decision for recycling and remanufacturing supply chain considering consumer online consumption preferences and recycled products’ quality
Bibliographic record
Abstract
With the organic integration of the Internet and remanufacturing industry, traditional manufacturers can recycle used products and sell products (including new and remanufactured products) through e-commerce retail platforms. A recycling and remanufacturing supply chain with three members (manufacturer, e-commerce retail platform, third-party recycler) is constructed in this paper. Manufacturer has remanufacturing capabilities, and the e-commerce retail platform can provide logistics service. In response to the organic integration of the Internet and remanufacturing industry, we mainly consider the pricing decisions of consumer preferences for online sales models and recycled products’ quality. Based on the impact of consumer used product recycling promotion activities on supply chain, a pricing game model was constructed for three recycling channels: manufacturer, e-commerce retail platform, and third-party recycler. Optimal pricing decision, logistics service level, used product recycling promotion intensity index, and recycling rate were obtained. Research has shown that consumer preferences can significantly improve supply chain logistics service level, pricing, market demand, and profits; Strengthening consumer awareness of remanufacturing used products and improving recycled products’ quality can not only lower consumer purchase prices and expand consumer demand, but also increase profits of supply chain members.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".