A new mathematical model for cellular manufacturing system with productivity consideration
Why this work is in the frame
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Bibliographic record
Abstract
In today’s environment of escalating competition, companies are adapting their management and production strategies, and product diversity is rapidly increasing. Companies require cellular manufacturing systems to produce products with high diversity in a short amount of time, ensuring the desired quality and meeting customer expectations. Cellular manufacturing systems, which have a more flexible structure compared to traditional production systems, are a good and effective solution for managers. Cellular manufacturing is an approach that aims to produce products with varying diversity in the shortest possible time and at the lowest cost, targeting an increase in efficiency. In this study, a cell manufacturing system proposal is made and cell formation is carried out to increase efficiency and effectiveness in a company that manufactures industrial refrigeration cabinets. A productivity-based 0-1 integer mathematical programming model is prepared that facilitates the simultaneous grouping of part and machine families in cell formation. In addition to the intracellular and intercellular transportation costs found in productivity-based models in the literature, labor costs, maintenance costs, the depreciation costs of the machines used in the cells, and the waiting costs of the machines are also added to the prepared model. The model is solved with the help of the GAMS 23.5.1 software package, creating part families and machine groups. Group efficiency values are measured, and the current and proposed situations are compared.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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