Optimal Part Family and Production Module Planning for Reconfigurable Manufacturing Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Manufactures are adopting Reconfigurable Manufacturing Systems (RMS) to better cope with frequently changing market conditions, which place tremendous demands on a system’s flexibility as well as its cost-effectiveness. Considerable efforts have been devoted to the development of necessary tools for the system level design and performance improvement, resulting in approaches to designing a single RMS. In this paper, a methodology for cost-effective reconfiguration planning for multi-module-multi-product RMS’s that best reflect the market demand changes is proposed. Formulated as an optimization procedure, reconfiguration planning is defined as the best reallocation of part families to production modules in an RMS and the best rebalancing of the whole system and each individual module to achieve minimum related cost and simultaneously satisfy the market demand. A Genetic Algorithm (GA) approach is proposed to overcome the computational difficulties caused by the problem complexity. Effectiveness of the proposed methodology is demonstrated with a case study.
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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