AGGREGATE PRODUCTION PLANNING UTILIZING A FUZZY LINEAR PROGRAMMING
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".