Summary of Review Of Cloud Service Composition For Intelligent Manufacturing, by Cuixia Li et al.
Review of Cloud Service Composition for Intelligent Manufacturing
by Cuixia Li, Liqiang Liu, Li Shi
First submitted to arxiv on: 3 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new approach to optimizing cloud services for intelligent manufacturing platforms is presented in this paper. The study focuses on improving the efficiency and quality of production by leveraging advanced technologies like AI and big data. To achieve sustainable development, 11 optimization indicators were defined, considering three-party participant subjects. Two categories of service optimization algorithms are classified: heuristic and reinforcement learning. The current key techniques are discussed, highlighting research hotspots and future trends. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making manufacturing better using new technologies like AI and big data. It shows how to optimize cloud services so that factories can run more efficiently and productively. The researchers defined 11 important indicators for measuring success and found two main ways to optimize: one uses rules, the other uses learning. They discuss what works now and where they think research should go next. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
