A Service Selection Approach in Cloud Manufacturing for SMEs

  • Haijiang Wu
  • Dan Ye
  • Shanshan Liu
  • Yan Yang
  • Lin Bai
Conference paper
Part of the Proceedings of the I-ESA Conferences book series (IESACONF, volume 7)


Small- and Medium-sized Enterprises benefits much from Service-oriented manufacturing, which utilizes the internet and service platform to arrange manufacturing resource and provides service according to the customers’ demands. On this platform, service selection is one of the key steps for customers to get the best services. This paper introduces a service selection approach in cloud manufacturing for Small- and Medium-size Enterprises. First, we build a service selection model, including service evaluation and service constraints. And then a service selection algorithm is presented based on the service selection model to identify the best service for a service buyer. Finally, a case study is given to illustrate how this approach works in Cloud Manufacturing Platform for Small- and Medium-sized Enterprises (CMfg-SME).


Cloud manufacturing Service selection Small- and medium-sized enterprises 



This work has been partly funded by the National Natural Science Foundation of China under grant 61170074, the Chinese National Hi-Tech. R&D Program under grant 2012AA011204, and the National Key Technology R&D Program under grant 2012BAF11B04–4. The authors wish to acknowledge the Commission for their support.


  1. 1.
    Tao, F., Cheng, Y., Zhang, L., et al. (2011). Cloud manufacturing. Advanced Materials Research, 201, 672–676.CrossRefGoogle Scholar
  2. 2.
    Huang, B., et al. (2013). Cloud manufacturing service platform for small-and medium-sized enterprises. The International Journal of Advanced Manufacturing Technology, 65(9–12), 1261–1272.Google Scholar
  3. 3.
    Wada, H., et al. (2012). E3: A multiobjective optimization framework for SLA-aware service composition. IEEE Transactions on Services Computing, 5(3):358–372.Google Scholar
  4. 4.
    Kuzu, M., & Nihan Kesim, C. (2012). Dynamic planning approach to automated web service composition. Applied Intelligence, 36(1), 1–28.Google Scholar
  5. 5.
    Feng, Z., et al. (2013). QoS-aware and multi-granularity service composition. Information Systems Frontiers, 15(4), 553–567.Google Scholar
  6. 6.
    Haddad, S., Mokdad, L., & Youcef, S. (2010). Selection of the best composite web service based on quality of service. ISSS/BPSC, 10, 255–266.Google Scholar
  7. 7.
    Huang, S., et al. (2011). Optimal service selection and composition for service-oriented manufacturing network. International Journal of Computer Integrated Manufacturing, 24(5), 416–430.Google Scholar
  8. 8.
    Tao, F., Zhao, D., & Zhang, L. (2010). Resource service optimal-selection based on intuitionistic fuzzy set and non-functionality QoS in manufacturing grid system. Knowledge and Information Systems, 25(1), 185–208.CrossRefGoogle Scholar
  9. 9.
    Shen, W., et al. (2007). An agent-based service-oriented integration architecture for collaborative intelligent manufacturing. Robotics and Computer-Integrated Manufacturing, 23(3), 315–325.Google Scholar
  10. 10.
    Shanshan, L. (2012). Research and implementation of cloud manufacture service selection for medium-sized and small enterprise. Master thesis. University of Chinese Academy of Sciences.Google Scholar
  11. 11.
    Goldberg, D. E., Korb, B., & Deb, K. (1989). Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems, 5(3), 493–530.MathSciNetGoogle Scholar
  12. 12.
    Zheyuan, J., Jianghong, H., & Zhao, W. (2009). An optimization model for dynamic QoS-aware web services selection and composition. Chinese Journal of Computers, 32(5), 1014–1025.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Haijiang Wu
    • 1
    • 2
  • Dan Ye
    • 1
  • Shanshan Liu
    • 1
  • Yan Yang
    • 1
  • Lin Bai
    • 1
  1. 1.Institute of SoftwareChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations