Production and quality control of Hybrid Manufacturing Remanufacturing System with stochastic return
Bibliographic record
Abstract
This paper proposes a simulation-based optimization model addressing production and quality control in Hybrid Manufacturing Remanufacturing Systems (HMRS). Specifically, the challenging issue, in the remanufacturing context, of quality control under the stochastic nature of product returns is addressed. Experiments are conducted to measure the effect of the return rate variability on the system performances. The main finding is that when this variability is low, the impact of return rate uncertainty on the system performances remains minor. Hence investing in the costly market-driven product acquisition approach may be useless for a company which is not confronted to high variability in product returns. However, results showed that, when this return rate variability increases the critical stock level and optimal quality control changed considerably. Consequently, for a company that experiences such variability, acquiring returned products through the classic waste stream system may result in considerable increase of the total production cost.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".