AGGREGATE PRODUCTION PLANNING UTILIZING A FUZZY LINEAR PROGRAMMING
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Bibliographic record
Abstract
Uncertainties and imprecise information regarding customer demands, production, inventory, and MRP are very common in performing aggregate production-planning (APP) for the manufacturing in real world. This study presents a fuzzy linear programming approach for managing the uncertainties and imprecise information involved in industrial APP applications. Detailed discussions are given to the establishment of the Fuzzy Linear Programming approach with converting the fuzzy constraints of uncertain and imprecise items into deterministic equivalents. A mathematical model is developed for APP practice with the Fuzzy Linear Programming approach. For numerically performing an aggregate production planning with the Fuzzy Linear Programming developed, a computer simulation for an actual aggregate production-planning is presented. It is demonstrated in the study, the employment of the Fuzzy Linear Programming provides a great advantage in APP of manufacturing, if the parameters of the stochastic factors involved in the production planning are neither definitely reliable nor precise. The present study shows that the interrelated effects of the customer service level and facility capacity on the effectiveness and efficiency of aggregate production-planning is significant and should be taken into account in performing an aggregate production-planning
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it