Virtual cellular manufacturing system based on resource element approach and analyzing its performance over different basic layouts
Why this work is in the frame
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Bibliographic record
Abstract
This research aims to present how choosing a suitable layout can improve the performance of virtual cellular manufacturing systems (VCMSs), especially minimizing the material flow between machines required by each family group. To present the efficacy of basic layouts on performances of VCMSs, a multi-objective mathematical model with a goal programming (GP) approach is developed to generate VCMSs based on resource-elements (REs). The formulated model is coded in Lingo software and is run over functional and distributed arrangements of the same machines. The performance and the validity of the developed model are checked by a numerical example taken from the literature. The objective function of the mathematical model is measured for that example over two mentioned layout to compare the performance of the generated systems. Moreover, because of the material handling costs importance, material flows are measured to find the best option as a basic layout for VCMSs. To compare the performance of the generated system with the classical cellular manufacturing system (CMS), cell capacity utilization (CCU) is employed as an independent criterion to evaluate each system. The result illustrates the priority of distributed layouts for generating RE-based VCMSs because of its flexibility, minimizing the objective function for the mathematical model, and smaller material flow by the components. In addition, the generated VCMSs outperforms the classical CMS from the CCU point of view.
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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