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Cloud Edge Collaborative Service Composition Optimization for Intelligent Manufacturing
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AI
DOI：10.1109/TII.2022.3208090.png)
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Abstract
En 中文
Service uncertainty modeling is an important problem of manufacturing service composition optimization, this article proposes a cloud manufacturing service composition optimization framework based on cloud-edge collaboration considering manufacturing service uncertainty. In the proposed framework, on the edge side, a model parameters estimation method of the manufacturing services' uncertainty is proposed based on Gaussian mixture regression; while on the cloud side, an intelligent evolutionary algorithm is adopted to effectively optimize the manufacturing service composition. Since the Gaussian mixture distribution is used to approximate the service availability distribution, the service uncertainty can be modeled adaptively. Compared with the previous optimization methods of manufacturing service composition with uncertainty based on the deterministic parameter models, the method proposed in this article can model the uncertainty of service more effectively, thus obtain better service composition solutions. Extensive experimental results prove the effectiveness of the algorithm.
Keywords:
Manufacturing
Uncertainty
Optimization methods
Costs
Collaboration
Production
Supply chains
Cloud edge collaboration
cloud manufacturing
service composition
uncertain service
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