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SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition

Author

Listed:
  • Tianyang Li

    (Northeast Electric Power University, Jilin
    Harbin Institute of Technology)

  • Ting He

    (Huaqiao University)

  • Zhongjie Wang

    (Harbin Institute of Technology)

  • Yufeng Zhang

    (University of Birmingham)

Abstract

Cloud manufacturing (CMfg) is a new service-oriented manufacturing paradigm in which shared resources are integrated and encapsulated as manufacturing services. When a single service is not able to meet some manufacturing requirement, a composition of multiple services is then required via CMfg. Service composition and optimal selection (SCOS) is a key technique for creating an on-demand quality of service (QoS)-optimal efficient manufacturing service composition to satisfy various user requirements. Given the number of services with the same functionality and a similar level of QoS, SCOS has been seen as a key challenge in CMfg research. One effective approach to solving SCOS problems is to use service domain features (SDF) through investigating the probability of services being used for a specific requirement from multiple perspectives. The approach can result in a division of the service space and then help streamline the service space with large-scale candidate services. The approach can also search for optimal subspaces that most likely contribute to an overall optimal solution. Accordingly, this paper develops an SDF-oriented genetic algorithm to effectively create a manufacturing service composition with large-scale candidate services. Fine-grained SDF definitions are developed to divide the service space. SDF-based optimization strategies are adopted. The novelty of the proposed algorithm is presented based on Bayes’ theorem. The effectiveness of the proposed algorithm is validated by solving three real-world SCOS problems in a private CMfg.

Suggested Citation

  • Tianyang Li & Ting He & Zhongjie Wang & Yufeng Zhang, 2020. "SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 681-702, March.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:3:d:10.1007_s10845-019-01472-1
    DOI: 10.1007/s10845-019-01472-1
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    References listed on IDEAS

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    1. Hong Jin & Xifan Yao & Yong Chen, 2017. "Correlation-aware QoS modeling and manufacturing cloud service composition," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1947-1960, December.
    2. F. Tao & Y. Cheng & L. Zhang & A. Y. C. Nee, 2017. "Advanced manufacturing systems: socialization characteristics and trends," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1079-1094, June.
    3. Hua Guo & Lin Zhang & Yilong Liu & Fei Tao & Min Shu & Shaomin Mu, 2014. "A discovery method of service-correlation for service composition in virtual enterprise," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 8(5), pages 579-618.
    4. Tao, Fei & Zhao, Dongming & Yefa, Hu & Zhou, Zude, 2010. "Correlation-aware resource service composition and optimal-selection in manufacturing grid," European Journal of Operational Research, Elsevier, vol. 201(1), pages 129-143, February.
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    Cited by:

    1. Jiajun Zhou & Liang Gao & Chao Lu & Xifan Yao, 2025. "Collaborative optimization of manufacturing service allocation via multi-task transfer learning evolutionary approach," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 1761-1779, March.
    2. Reza Vatankhah Barenji, 2022. "A blockchain technology based trust system for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1451-1465, June.
    3. Hongbin Wang & Yang Ding & Hanchuan Xu, 2024. "Particle swarm optimization service composition algorithm based on prior knowledge," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 35-53, January.

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