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An optimization method of cloud manufacturing service composition based on matching-collaboration degree

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Abstract

Traditional service combination methods in the cloud manufacturing paradigm mainly focus on economic targets, such as time and cost and ignore the matching and collaboration effects between cloud services and manufacturing tasks, resulting in the constructed service combination solutions not fully meeting the individual requirements of users. In this paper, the concepts of service matching degree and service collaboration degree are proposed, and an evaluation system of cloud manufacturing service composition is established to measure the quality of cloud services. Then a double-constraint service composition optimization model is designed considering the interests of both manufacturing service requestors and resource providers, which is solved by using the improved ant colony algorithm (IACO). Finally, an automobile bumper cloud manufacturing case is carried out to demonstrate the feasibility and effectiveness of the proposed method.

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Funding

The authors gratefully acknowledge financial support from the National Key R&D Program of China (No. 2022YFB3305603), the National Natural Science Foundation of China (No. 52075060, No. 51875065), and the Fundamental Research Funds for the Central Universities (No. 2023CDJXY-021).

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Author notes

  1. Chao Yin and Shanglin Li contributed equally to this work.

Authors and Affiliations

  1. College of Mechanical and Vehicle Engineering, Chongqing University, Shazheng Street, Shapingba District, Chongqing, 400044, China

    Chao Yin, Shanglin Li & Xiaobin Li

  2. State Key Laboratory of Mechanical Transmissions, Chongqing University, Shazheng Street, Shapingba District, Chongqing, 400044, China

    Chao Yin, Shanglin Li & Xiaobin Li

Authors
  1. Chao Yin
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  2. Shanglin Li
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  3. Xiaobin Li
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Contributions

Chao Yin and Xiaobin Li contributed to the core ideas and concepts of the paper. The method design, material preparation, case data collection, and analysis were performed by Xiaobin Li and Shanglin Li. The first and final revised version of the manuscript was accomplished by Xiaobin Li and Shanglin Li. All authors read and approved the manuscript.

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Correspondence to Xiaobin Li.

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Yin, C., Li, S. & Li, X. An optimization method of cloud manufacturing service composition based on matching-collaboration degree. Int J Adv Manuf Technol 131, 343–353 (2024). https://doi.org/10.1007/s00170-024-13119-4

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  • DOI: https://doi.org/10.1007/s00170-024-13119-4

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