Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information
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 purpose of this paper is to advance the similarity coefficient method to solve cell formation (CF) problems in two aspects. Firstly, while numerous similarity coefficients have been proposed to incorporate different production factors in literature, a weighted sum formulation is applied to aggregate them into a nonbinary matrix to indicate the dependency strength among machines and parts. This practice allows flexible incorporation of multiple production factors in the resolution of CF problems. Secondly, a two-mode similarity coefficient is applied to simultaneously form machine groups and part families based on the classical framework of hierarchical clustering. This practice not only eliminates the sequential process of grouping machines (or parts) first and then assigning parts (or machines), but also improves the quality of solutions. The proposed clustering method has been tested through twelve literature examples. The results demonstrate that the proposed method can at least yield solutions comparable to the solutions obtained by metaheuristics. It can yield better results in some instances, as well.
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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 | 0.001 |
| 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