Multi-centric management and optimized allocation of manufacturing resource and capability in cloud manufacturing system
Bibliographic record
Abstract
Cloud manufacturing offers the potential to make mass manufacturing resources and capabilities more widely integrated and accessible to users through network. Most related research assumes that there exists only one management center for all manufacturing resources and capabilities in a manufacturing cloud. However, this could cause the efficiency problem (e.g. scheduling time) and harm the quality of service (e.g. response time). Actually, a large-scale manufacturing cloud should have multiple management centers to deal with massive, widely distributed manufacturing resources and capabilities and users; meanwhile, the constraint of finite manufacturing resources and capabilities and the cost of remote collaboration should be taken into consideration. Thus, this article first presents the architecture for the multi-centric management with two-level scheduling strategy combining the advantages of the centralized and decentralized decision-making. Then, after quantifying the availability and the collaborative cost of the manufacturing resources and capabilities, we propose a global optimization model for the manufacturing resources and capability allocation under the multi-centric architecture. Finally, a case study adopting our new method shows that the utilization of the manufacturing resources and capabilities would be more balanced, while the cost of the total collaboration would be reduced, compared with the typical decentralized solution. The research results can support cloud manufacturing to effectively deal with the challenge of management and allocation for increasingly large-scale and distributed manufacturing resources and capabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".