A cost-based model to select best capacity scaling policy for reconfigurable manufacturing systems
Why this work is in the frame
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Bibliographic record
Abstract
This study presents a system dynamics approach to model and analyse a single stage reconfigurable manufacturing system (RMS). The system is exposed to a random demand that follow a normal distribution. New modifications to the existing state of the art capacity scaling model are applied to bring it closer to reality. A module to account capacity scaling costs and a module for considering seasonal demand are introduced. The objective of this study is to evaluate the performance of different capacity scaling policies for various system scenarios. Experimentations are applied on three stages; preliminary experimentation, Taguchi fractional factorial design, and 24 full factorial design to conduct various system scenarios. Two policy selection rules are produced to help a practitioner in deciding the best scaling policy according to the existing system scenario. The results showed that chasing demand policy and inventory-based policy have the best performance for most system scenarios. [Received 13 March 2014; Revised 26 November 2014; Accepted 26 January 2015]
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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