Estimation-based production control of manufacturing–remanufacturing systems with uncertain seasonal return and imprecise demand and inventory
Bibliographic record
Abstract
Hybrid manufacturing systems utilising raw materials and returned end-of-life products for production are studied. The systems are failure-prone and subject to inventory, market demand and return uncertainties. Thanks to growing environmental and sustainability concerns, the manufacturing sector is currently experiencing a significant growth in the popularity of reverse logistics. However, the practical implementation of production control in such systems is challenging due to return flow uncertainty and variability. To address this challenge, an estimation-based control using the Kalman filter is proposed in this research. The demand and return models employed contain random and deterministic components, with the latter being time-invariant for demand and uncertain with seasonal variations for return. The processing steps used include the estimation of inventory levels and demand and return components, return forecasting allowing cost computation over a long horizon, and the determination of the production and disposal policies adapting to market variations and uncertainties. We classify the systems according to relationships between their production capacity, demand and return ranges. We then present an extensive numerical study of optimal policies for various system classes and show that adaptive policies outperform the conventional ones, thus proving the effectiveness of the proposed production control approach for complex industrially oriented systems.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".