Cloud manufacturing resource optimization allocation method based on improved sparrow search algorithm

Chao Yin; Chang Xiong; Hao Peng

doi:10.1117/12.3050812

27 September 2024 Cloud manufacturing resource optimization allocation method based on improved sparrow search algorithm

Chao Yin, Chang Xiong, Hao Peng

Author Affiliations +

Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 1328103 (2024) https://doi.org/10.1117/12.3050812
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China

Abstract

Cloud manufacturing was a new type of manufacturing model, and a cloud task decomposition method and a service composition optimization method were proposed to address the problem of cloud manufacturing resource optimization allocation. Firstly, a correlation matrix was established based on the information correlation between atomic tasks, then hierarchical clustering algorithm was used to reorganize the atomic tasks, and finally the decomposition of the total tasks was realized. For the service composition problem of cloud manufacturing, an improved sparrow search algorithm (ISSA) was proposed. The ISSA adopted the Latin hypercube sampling method to generate an initialized population and incorporated the golden sine operator and the opposition-based learning strategy to avoid the algorithm from falling into a local optimum. Finally, an example of Bluetooth headset manufacturing was used to validate the resource optimization allocation method proposed in this paper, which demonstrated the feasibility of the method proposed.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chao Yin, Chang Xiong, and Hao Peng "Cloud manufacturing resource optimization allocation method based on improved sparrow search algorithm", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 1328103 (27 September 2024); https://doi.org/10.1117/12.3050812

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