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

  • Tianyang Li
  • Ting HeEmail author
  • Zhongjie Wang
  • Yufeng Zhang
Article
  • 39 Downloads

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.

Keywords

Service domain features (SDFs) Service composition and optimal selection (SCOS) Cloud manufacturing (CMfg) Manufacturing cloud service composition Genetic algorithm (GA) 

Notes

Acknowledgements

This work has been supported in part by the research projects the National Natural Science Foundation of China (NSFC) (No. 71571056) and the Scientific Research Funds of Huaqiao University (16BS304).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Tianyang Li
    • 1
    • 2
  • Ting He
    • 3
    Email author
  • Zhongjie Wang
    • 2
  • Yufeng Zhang
    • 4
  1. 1.School of Computer ScienceNortheast Electric Power University, JilinJilinChina
  2. 2.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  3. 3.College of Computer Science and TechnologyHuaqiao UniversityXiamenChina
  4. 4.Birmingham Business SchoolUniversity of BirminghamBirminghamUK

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