Multi-agent Modeling of the Collaborative Operation of the Producer Service Supply Chain under the Intelligent Manufacturing Clusters in the Yangtze River Delta
Bibliographic record
Abstract
With the proliferation and aggregation of advanced production technology and information technology, several regional intelligent manufacturing clusters have taken shape in China. The development of intelligent manufacturing, which is highly technical, innovative, and informatized, needs the support of more professional producer services. Considering the broad scope of producer service supply-demand in regional intelligent manufacturing, this paper takes the intelligent manufacturing cluster in the Yangtze River Delta as the object, analyzes the limited access to producer services required for production operations, and proposes to integrate and supply the resources of multiple service agents in the region in a dynamic and collaborative manner. Then, the Multi-Agent model was constructed for the producer service supply chain, and the relevant factor libraries and decision libraries were designed. Finally, an empirical analysis was carried out to evaluate the stability of the collaborative operation of the producer service supply chain under the effect of some collaboration factors. The results show that the model has a certain theoretical reference value.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".