Similarity Evaluation Based on Intuitionistic Fuzzy Set for Service Cluster Selection as Cloud Service Candidate

  • Jorick Lartigau
  • Xiaofei Xu
  • Lanshun Nie
  • Dechen Zhan
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 144)


Cloud manufacturing (CMfg) provides new opportunities toward the servitization, and embeds a set of functional features to enhance the collaboration among various service providers and their resources. The main target is to compose dedicated manufacturing cloud, by encompassing a set of cloud services, to manufacture a requested service. CMfg is a recent concept, but already widely spread in the academic and industrial researches in China. The paper firstly focuses on the manufacturing environment background to understand its purpose. Thus as an introduction, the concept of CMfg is discussed. Finally, we present a method based on intuitionistic fuzzy set for the similarity evaluation between cloud services and service clusters. The objective is to match the best service cluster to provide composite resource services as cloud service candidates. Our method is ABC (Artificial Bee Colony) optimized, and its performance are discussed through experiments.


Cloud manufacturing (CMfg) Service cluster Cloud service Intuitionistic fuzzy set (IFS) Artificial bee colony (ABC) 


  1. 1.
    Tao, F., Hu, Y.F., Zhou, Z.D.: Study on manufacturing grid and its resource service optimal-selection system. Int. J. Adv. Mfg Technology 37, 1022–1041 (2008)CrossRefGoogle Scholar
  2. 2.
    Tao, F., Zhang, L., Venkatesh, V.C., Luo, Y., Cheng, Y.: Cloud manufacturing: a computing and service-oriented manufacturing model. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 1–8 (August 2011)Google Scholar
  3. 3.
    Ren, G., Gregory, M.: Servitization in manufacturing Companies – Literature Review, Research Progress and Cambridge service Research. Cranfield Product-Service Systems Seminar (November 2007)Google Scholar
  4. 4.
    Neely, A., Benedetinni, O., Visnjic, I.: The servitization of manufacturing: Further evidence. In: 18th Euro. Ops. Management Association Conf., Cambridge (July 2011)Google Scholar
  5. 5.
    Loughridge, M.: IBM Financial Model. IBM Investor Briefing (2010)Google Scholar
  6. 6.
    Ning, F., Zhou, W., Zhang, F., Yin, Q., Ni, X.: The Architecture of Cloud manufacturing and its key technologies research. In: Proceedings of IEEE Cloud Computing and Intelligence Systems (CCIS), pp. 259–263 (September 2011)Google Scholar
  7. 7.
    Buyya, R., Yeo, C.S., Venugopal, S.: Market-Oriented Cloud Computing: Vision, Hype, and reality for delivering it services as Computing Utilities, vol. 07, pp. 7–9. University of Melbourne, Australia (2008)Google Scholar
  8. 8.
    Xu, X.: From Cloud computing to Cloud manufacturing. Rob. and Comp. Mfg (July 2011)Google Scholar
  9. 9.
    Mou, T., Nie, L., Zhan, D., Xu, X.: Task Scheduling and Assignment Methods for Cloud Enterprises. In: Enterprise Interoperability V, pp. 427–437 (2012)Google Scholar
  10. 10.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–553 (1965)CrossRefGoogle Scholar
  11. 11.
    Liang, L.R., Lu, S., Wang, X., Lu, Y., Mandal, V., Patacsil, D., Kumar, D.: FM-test: A Fuzzy-Set-Theory-Based Approach to Differential Gene Expression Data Analysis. BMC Bioinformatics 7(4) (2006)Google Scholar
  12. 12.
    Atanassov, K.T.: Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems, 87–96 (1986)Google Scholar
  13. 13.
    Tao, F., Zhao, D., Zhang, L.: Resource service optimal-selection based on intuitionistic fuzzy set and non-functionality QoS in manufacturing grid system. Knowledge and Information Systems 25(01), 185–208 (2010)CrossRefGoogle Scholar
  14. 14.
    Xu, Z.: Some similarity evaluations of intuitionistic fuzzy sets and their applications to multiple attribute decision making. Fuzzy Optim. Decision Making 6(2), 109–121 (2007)CrossRefGoogle Scholar
  15. 15.
    Genari, A.C., Guliato, D.: Similarity evaluations based on fuzzy sets. Federal University of Uberlandia, Brazil Google Scholar
  16. 16.
    Karaboga, D., Akay, B.: A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation 214(1), 108–132 (2009)CrossRefGoogle Scholar
  17. 17.
    Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review (March 2012)Google Scholar
  18. 18.
    Anandhakumar, R., Subramanian, S., Ganesan, S.: Modified ABC Algorithm for Generator Maintenance Scheduling. Int. J. of Comp. and Elec. Engineering 3(6), 812–819 (2011)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Jorick Lartigau
    • 1
  • Xiaofei Xu
    • 1
  • Lanshun Nie
    • 1
  • Dechen Zhan
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina

Personalised recommendations