Part-level Sequence Dependent Setup Time Reduction in CMS
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents the idea of creating cells while reducing part-level sequence-dependent setup time in general cellular manufacturing systems (CMS). Setup time reduction in CMS has gained modest attention in the literature. This could be attributed to the fact that the fundamental problem in cell formation in CMS has been mainly related to material handling and machine utilization while setup time was assumed to implicitly decrease as a result of grouping similar parts in a manufacturing cell. Despite more than three decades of CMS’s history, it has been relatively recent that setup time has been included in cell formation problems and found a place in the existing models. However, sequence-dependent setup time in the literature has been dealt with mostly within the context of scheduling “part-families” in a single manufacturing cell or in the allocation of parts to flow line cells. The present model includes the three fundamental elements of a cell formation procedure: machine utilization, intercellular movement and setup time. This therefore provides a basic structure that would serve as a general sub-model for real manufacturing cell formation problems including any type of setup time and manufacturing cell. Due to computation time and complexity of the problem, a solution approach based on theGenetic Algorithm based (GA-based)heuristic has been discussed and the solution of a sample problem has been compared with that of conventional optimization software. The results indicate a reasonably satisfactory performance by the GAbased heuristic in terms of accuracy and computation time.
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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