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A collaborative service group-based fuzzy QoS-aware manufacturing service composition using an extended flower pollination algorithm

  • Shuai Zhang
  • Wenting Yang
  • Wenyu Zhang
  • Mingzhou Chen
Original Paper
  • 26 Downloads

Abstract

Quality of service (QoS)-aware manufacturing service composition has attracted growing attention from experts and scholars. However, most of them ignore the fuzziness and complexity of QoS values and describe QoS values using precise numbers. In addition, a one-to-one mapping-based service composition method has been widely used making it difficult to obtain an optimal solution with higher QoS values. In this study, we construct a new collaborative service group-based fuzzy QoS-aware (CSGFQ) manufacturing service composition model, which not only expands the traditional one-to-one mapping-based relationship between services and subtasks, but also objectively describes QoS values using fuzzy numbers. An extended flower pollination algorithm (FPA) that embeds four improvements is presented to solve the corresponding model. Four groups of experiments are performed to compare our proposed method with other baseline algorithms to prove the practicality, effectiveness, efficiency, and other performance of the extended FPA in solving the CSGFQ service composition problem.

Keywords

Manufacturing service composition Collaborative service groups Fuzzy QoS-aware Extended flower pollination algorithm 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 51475410, 51875503, 51775496) and Zhejiang Natural Science Foundation of China (No. LY17E050010).

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this article.

Ethical standard

The authors state that this research complies with ethical standards. This research does not involve either human participants or animals.

References

  1. 1.
    Xue, X., Wang, S., Lu, B.: Manufacturing service composition method based on networked collaboration mode. J. Netw. Comput. Appl. 59, 28–38 (2016)CrossRefGoogle Scholar
  2. 2.
    Zhang, W.Y., Zhang, S., Guo, S., et al.: Concurrent optimal allocation of distributed manufacturing resources using extended teaching-learning-based optimization. Int. J. Prod. Res. 55(3), 718–735 (2017)CrossRefGoogle Scholar
  3. 3.
    Zhang, S., Xu, S., Zhang, W.Y., et al.: A hybrid approach combining an extended BBO algorithm with an intuitionistic fuzzy entropy weight method for QoS-aware manufacturing service supply chain optimization. Neurocomputing 272, 439–452 (2017)CrossRefGoogle Scholar
  4. 4.
    Zhou, J., Yao, X.: A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition. Int. J. Prod. Res. 55(16), 1–20 (2017)CrossRefGoogle Scholar
  5. 5.
    Tao, F., Zhao, D., Yefa, H., et al.: Correlation-aware resource service composition and optimal-selection in manufacturing grid. Eur. J. Oper. Res. 201(1), 129–143 (2010)CrossRefzbMATHGoogle Scholar
  6. 6.
    Zhang, W.Y., Zhang, S., Cai, M., et al.: A new manufacturing resource allocation method for supply chain optimization using extended genetic algorithm. Int. J. Adv. Manuf. Technol. 53(9), 1247–1260 (2011)CrossRefGoogle Scholar
  7. 7.
    Xiang, F., Hu, Y., Yu, Y., et al.: QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system. Central Eur. J. Oper. Res. 22(4), 663–685 (2014)CrossRefzbMATHGoogle Scholar
  8. 8.
    Liu, W., Liu, B., Sun, D., et al.: Study on multi-task oriented services composition and optimisation with the ‘Multi-Composition for Each Task’ pattern in cloud manufacturing systems. Int. J. Comput. Integr. Manuf. 26(8), 786–805 (2013)CrossRefGoogle Scholar
  9. 9.
    Liu, B., Zhang, Z.: QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups. Int. J. Adv. Manuf. Technol. 88(9–12), 2757–2771 (2017)CrossRefGoogle Scholar
  10. 10.
    Tao, F., Zhao, D., Hu, Y., et al.: Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. IEEE Trans. Ind, Inform. 4(4), 315–327 (2008)CrossRefGoogle Scholar
  11. 11.
    Zhang, L., Zhu, Y., Zheng, W.X.: Synchronization and state estimation of a class of hierarchical hybrid neural networks with time-varying delays. IEEE Trans. Neural Netw. Learn. Syst. 27(2), 459–470 (2016)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Zhang, L., Zhu, Y., Wei, X.Z.: State estimation of discrete-time switched neural networks with multiple communication channels. IEEE Trans. Cybern. 47(4), 1028–1040 (2017)CrossRefGoogle Scholar
  13. 13.
    Sakawa, M., Mori, T.: An efficient genetic algorithm for job-shop scheduling problems with fuzzy processing time and fuzzy duedate. Comput. Ind. Eng. 36(2), 325–341 (1999)CrossRefGoogle Scholar
  14. 14.
    Sakawa, M., Kubota, R.: Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms. Eur. J. Oper. Res. 120(2), 393–407 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Zhang, R., Song, S., Wu, C.: A hybrid differential evolution algorithm for job shop scheduling problems with expected total tardiness criterion. Appl. Soft Comput. J. 13(3), 1448–1458 (2013)CrossRefGoogle Scholar
  16. 16.
    Yang, X.S.: Flower pollination algorithm for global optimization. In: Proceedings of the 17th International Conference on Unconventional Computation and Natural Computation. Orléans, France. 29–30 Sept, pp. 240–249, (2012)Google Scholar
  17. 17.
    Deng, S., Wu, H., Hu, D., et al.: Service selection for composition with QoS correlations. IEEE Trans. Serv. Comput. 9(2), 291–303 (2016)CrossRefGoogle Scholar
  18. 18.
    Huang, X., Du, B., Sun, L., et al.: Service requirement conflict resolution based on ant colony optimization in group-enterprises-oriented cloud manufacturing. Int. J. Adv. Manuf. Technol. 84(1), 183–196 (2016)CrossRefGoogle Scholar
  19. 19.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)CrossRefzbMATHGoogle Scholar
  20. 20.
    Cui, Y., Huang, M., Yang, S., et al.: Fourth party logistics routing problem model with fuzzy duration time and cost discount. Knowl. Based Syst. 50(1), 14–24 (2013)CrossRefGoogle Scholar
  21. 21.
    Zhong, Z., Zhu, Y., Lam, H.K.: Asynchronous piecewise output-feedback control for large-scale fuzzy systems via distributed event-triggering schemes. IEEE Trans. Fuzzy Syst. 26(3), 1688–1703 (2018)CrossRefGoogle Scholar
  22. 22.
    Laarhoven, P.J.M.V., Pedrycz, W.: A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst. 11(1–3), 199–227 (1983)MathSciNetzbMATHGoogle Scholar
  23. 23.
    Lam, K.C., Tao, R., Lam, M.C.K.: A material supplier selection model for property developers using fuzzy principal component analysis. Autom. Constr. 19(5), 608–618 (2010)CrossRefGoogle Scholar
  24. 24.
    Shaw, K., Shankar, R., Yadav, S.S., et al.: Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain. Expert Syst. Appl. 39(9), 8182–8192 (2012)CrossRefGoogle Scholar
  25. 25.
    Giachetti, R.E., Young, R.E.: A parametric representation of fuzzy numbers and their arithmetic operators. Fuzzy Sets Syst. 91(2), 185–202 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Abdel-Raouf, O., Abdel-Baset, M.: A new hybrid flower pollination algorithm for solving constrained global optimization problems. Int. J. Appl. Oper. Res. Open Access J. 4(2), 1–13 (2014)Google Scholar
  27. 27.
    Dubey, H.M., Pandit, M., Panigrahi, B.K.: Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch. Renew. Energy 83, 188–202 (2015)CrossRefGoogle Scholar
  28. 28.
    Abdelaziz, A.Y., Ali, E.S., Elazim, S.M.A.: Combined economic and emission dispatch solution using Flower Pollination Algorithm. Int. J. Electri. Power Energy Syst. 80, 264–274 (2016)CrossRefGoogle Scholar
  29. 29.
    Pan, J.S., Dao, T.K., Nguyen, T.T., et al.: Dynamic diversity population based flower pollination algorithm for multimodal optimization. In: Proceedings of the 8th Asian Conference on Intelligent Information and Database Systems. Da Nang, Vietnam, 14–16 Mar, pp. 440–448, (2016)Google Scholar
  30. 30.
    Yang, X.S., Karamanoglu, M., He, X.: Flower pollination algorithm: a novel approach for multiobjective optimization. Eng. Optim. 46(9), 1222–1237 (2014)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Wang, Y.J.: A fuzzy multi-criteria decision-making model based on simple additive weighting method and relative preference relation. Appl. Soft Comput. 30, 412–420 (2015)CrossRefGoogle Scholar
  32. 32.
    Wei, G.: Grey relational analysis model for dynamic hybrid multiple attribute decision making. Knowl. Based Syst. 24(5), 672–679 (2011)CrossRefGoogle Scholar
  33. 33.
    Zeng, L., Benatallah, B., Ngu, A.H.H., et al.: QoS-aware middleware for web services composition. IEEE Trans. Softw. Eng. 30(5), 311–327 (2004)CrossRefGoogle Scholar
  34. 34.
    Sun, Q.: An improved particle swarm optimization algorithm for QoS-aware web service selection in service oriented communication. Int. J. Comput. Intell. Syst. 3(sup01), 18–30 (2010)CrossRefGoogle Scholar
  35. 35.
    Coelho, L.D.S., Mariani, V.C.: Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Expert Syst. Appl. 34(3), 1905–1913 (2008)CrossRefGoogle Scholar
  36. 36.
    Alatas, B.: Chaotic bee colony algorithms for global numerical optimization. Expert Syst. Appl. 37(8), 5682–5687 (2010)CrossRefGoogle Scholar
  37. 37.
    Croes, G.A.: A method for solving traveling-salesman problems. Oper. Res. 6(6), 791–812 (1958)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Fogel, D.B.: An introduction to simulated evolutionary optimization. IEEE Trans. Neural Netw. 5(1), 3–14 (1994)CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of InformationZhejiang University of Finance and EconomicsHangzhouChina

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