Dynamic cellular manufacturing under multiperiod planning horizons
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The purpose of this research paper is to discuss cellular manufacturing is discussed under conditions of changing product demand. Traditional cell formation procedures ignore any changes in demand over time from product redesign and other factors. However given that in today's business environment, product life cycles are short, a framework is proposed that creates a multi‐period cellular layout plan including cell redesign where appropriate. Design/methodology/approach The framework is illustrated using a two‐stage procedure based on the generalized machine assignment problem and dynamic programming. This framework is conceptually compared to virtual cell manufacturing, which is useful when there is uncertainty in demand rather than anticipated changes in demand. A case study is used to explain how the concept would work in practice. Findings One major characteristic of the proposed method is that it is flexible enough to incorporate existing cell formation procedures. It is shown through an example problem that the proposed two‐stage method is better than undergoing ad hoc layout changes or ignoring the demand changes when shifting or cell rearrangement costs exist. It also sheds some insight into cellular manufacturing under dynamic conditions. Originality/value This paper should be useful to both researchers and practitioners who deal with demand changes in cellular manufacturing.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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