Comprehensive machine cell/part family formation using genetic algorithms
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
The solution quality of a comprehensive machine/part grouping problem, where the processing times, lot sizes and machine capacities are considered, may not be properly evaluated using a binary performance measure. This paper suggests a generalized grouping efficacy index which has been compared favorably with two binary performance measures. A genetic algorithm using the generalized performance measure as the objective is developed to solve the comprehensive grouping problems. The algorithm has been tested using a number of reference problems with processing times being randomly assigned to all operations. The effects of three major genetic parameters (population size, mutation rate and the number of crossover points) have also been examined. The results indicate that, when the computational time is fixed, larger population size and lower mutation rate tend to improve solution quality while the number of crossover points has no significant impact on the final solution.
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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 | 0.001 | 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