A unique mathematical programming algorithm for performance optimization of organizational indicators in manufacturing sector
Why this work is in the frame
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Bibliographic record
Abstract
This study presents an integrated algorithm for the evaluation and optimization of manufacturing systems by considering managerial and organizational performance indicators. The proposed algorithm is composed of data envelopment analysis (DEA), fuzzy DEA and statistical methods. In order to achieve the goals of this study, a set of 12 criteria were chosen to indicate the application of the integrated method. The results showed DEA results have lower mean absolute percentage error (MAPE) than that of the fuzzy DEA. This study also analyzes and weights the indicators, and the results showed “research and development investment to production value” and “education and training investment per employee” indicators are the most effective indicators. This is the first study that introduces a unique algorithm for managerial and organizational factors. Second, it can handle data uncertainty due to existence of fuzzy mathematical programming in the algorithm. Third, weights of indicators are identified through robust statistical algorithm.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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