Integrated Product-Platform design and Multi-Period Lot-Sizing for hybrid manufacturing with fuzzy demand and variant substitution
Why this work is in the frame
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Bibliographic record
Abstract
This study develops an integrated mathematical formulation for hybrid manufacturing, incorporating product platforms, multi-period lot-sizing, and fuzzy demand to address demand uncertainty and product variation challenges. Applying the fuzzy set theory, demand is modeled as fuzzy demand, providing a more effective approach to handling uncertainty than deterministic methods. The model includes a substitution strategy to accommodate dynamic changes in variant requirements, enhancing production flexibility. Additionally, based on the developed fuzzy optimization model, the fuzzy model is employed to train a regression model that predicts costs as a function of anticipated confidence levels. The proposed model is validated through a case study, demonstrating its effectiveness in minimizing total production costs and efficiently managing multiple product variants across different planning periods. The findings offer adaptive production planning strategies for manufacturers facing fluctuating demand and high product variety.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 it