Integrating static and dynamic analysis in studying capacity scalability in RMS
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Bibliographic record
Abstract
The purpose of this paper is to provide a deeper understanding about the relation between management level and operational level in capacity scalability problem in Reconfigurable Manufacturing Systems (RMS). The approach presented in this paper integrates both static and dynamic analyses. A dynamic model for capacity scalability in RMS is developed and then a study of optimal capacity scalability by means of multi-objective optimisation is discussed. The dynamic model is based on control theory where the capacity scalability system is modelled using block diagram and a transfer function is derived. The capacity scalability controller's gain is determined using multi-objective optimisation. Simulation study of an industrial case for a scenario of exogenous disturbance to RMS is conducted. Results showed the impact of management strategies on the performance of capacity scalability systems in RMS and also the importance of dynamic control for capacity scalability management in modern system like RMS.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| 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