Consideration of dynamic changes in machine reliability and part demand: a cellular manufacturing systems design model
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In a dynamic manufacturing and marketing environment, machine reliability is often subject to issues of usage and age, with areas like part demand experiencing frequent changes as well. It is vital, therefore, for a manufacturing cell design to consider and plan for such changes so that said design will continue to meet expectations in future applications. This study proposes a multi-objective, integer-programming (IP) model for designing a cellular manufacturing system (CMS) that will remain optimal for the entire multi-period planning horizon by considering dynamic changes in machine reliability and part demand over the periods. This model will allow for alternative part processing routes and select suitable machines along those routes – maximising machine system reliability and minimising system costs. This model also accounts for the purchase of new machine capacity when needed in an effort to design an optimal cell that remains suitable for the entire planning horizon. This study illustrates an -constraint solution procedure that will facilitate the user when selecting suitable solutions based on the importance they impart to the objectives. Keywords: cellular manufacturing systems (CMS)dynamic change in machine reliabilitydynamic parts demandexponential distributionmulti-objective multi-period modelsystem cost and system reliability Acknowledgement The authors would like to express their gratitude to the University of Windsor and the Natural Sciences and Engineering Research Council (NSERC) for the financial support during the tenure of this research project.
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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 | 0.002 | 0.001 |
| 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