Characteristics analysis and optimization design of entities collaboration for cloud manufacturing
Bibliographic record
Abstract
Summary By applying the cloud manufacturing paradigm in the regional enterprise cluster, the enterprises or facilities may collaborate extensively for efficiently utilizing the manufacturing resources. It is valuable to explore and design the strategies of facilities selection for the autonomic control of the collaboration behaviors in production. In this paper, we model the collaboration relations in the regional enterprise cluster as a generalized social collaboration network and explore the dynamic growth process of the Facilities Collaboration Network for different strategies of facilities selection, including the random selection with and without preference, and the balanced selection with and without preference. With performance indexes such as network size, the distribution of node degree and act degree, clustering coefficient, the average shortest distance, and the number of n ‐cliques, we present and analyze the characteristics of these strategies for cloud manufacturing. Next, based on these characteristics, we propose 2 mechanisms for self‐optimization in facilities collaboration, including the dynamic weighing of facilities and the concentrated processing of successive subtasks in the process. We also analyze the mechanisms' effects on the characteristics of Facilities Collaboration Network and the performance in manufacturing. Copyright © 2016 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".