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Journal of Intelligent Manufacturing

, Volume 30, Issue 5, pp 2069–2083 | Cite as

A new fuzzy QoS-aware manufacture service composition method using extended flower pollination algorithm

  • Shuai Zhang
  • Yangbing Xu
  • Wenyu ZhangEmail author
  • Dejian Yu
Article

Abstract

With the increasing complexity of manufacturing tasks and the exponential growth of candidate services, manufacturing service composition has become considerably challenging in relation to the integration of service supply chains in fuzzy manufacturing environments. Quality of service (QoS), as a popular index, is widely used to evaluate the fitness of solutions to the manufacturing service composition (SMSC). In this study, we first establish a new fuzzy QoS-aware mathematical model that considers the preferences of manufacturing enterprises by assigning different sub-tasks with different weights to evaluate the global fuzzy QoS of the SMSCs. We then extend the flower pollination algorithm (FPA) to obtain an optimal SMSC more effectively by making the switch probability self-adaptive, improving the local search ability, and adding the strategy of elite replacement. Finally, we demonstrate that the proposed extended FPA is an effective and efficient algorithm for solving the manufacturing service composition problem with differently weighted sub-tasks in a fuzzy manufacturing environment. We do this by comparing it with other well-known metaheuristic algorithms such as basic FPA, genetic algorithm, cuckoo search algorithm, and particle swarm optimization.

Keywords

Manufacturing service composition Fuzzy QoS Triangular fuzzy number Extended flower pollination algorithm 

Notes

Acknowledgements

The work has been supported by National Natural Science Foundation of China (Nos. 51475410, 51375429), Zhejiang Natural Science Foundation of China (No. LY17E050010).

References

  1. Amin, S. H., & Razmi, J. (2009). An integrated fuzzy model for supplier management: A case study of ISP selection and evaluation. Expert Systems with Applications, 36(4), 8639–8648.Google Scholar
  2. Cao, Y. L., Wu, Z. J., Liu, T., Gao, Z. B., & Yang, J. X. (2016). Multivariate process capability evaluation of cloud manufacturing resource based on intuitionistic fuzzy set. The International Journal of Advanced Manufacturing Technology, 84(1), 227–237.Google Scholar
  3. Chen, C. T., Lin, C. T., & Huang, S. F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102(2), 289–301.Google Scholar
  4. Kahraman, C., Cebeci, U., & Ruan, D. (2004). Multi-attribute comparison of catering service companies using fuzzy AHP: The case of Turkey. International Journal of Production Economics, 87(2), 171–184.Google Scholar
  5. Kanagaraj, G., Ponnambalam, S. G., & Jawahar, N. (2016). Reliability-based total cost of ownership approach for supplier selection using cuckoo-inspired hybrid algorithm. The International Journal of Advanced Manufacturing Technology, 84(5), 801–816.Google Scholar
  6. Karagöz, S., & Yildiz, A. R. (2017). A comparison of recent metaheuristic algorithms for crashworthiness optimisation of vehicle thin-walled tubes considering sheet metal forming effects. International Journal of Vehicle Design, 73(1–3), 179–188.Google Scholar
  7. Li, Q., Dou, R. L., Chen, F. Z., & Nan, G. F. (2014). A QoS-oriented web service composition approach based on multi-population genetic algorithm for internet of things. International Journal of Computational Intelligence Systems, 7(2), 26–34.Google Scholar
  8. Lin, Y. K., & Chong, C. S. (2017). Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system. Journal of Intelligent Manufacturing, 28(5), 1189–1201.Google Scholar
  9. Liou, T. S., & Wang, M. J. J. (1992). Ranking fuzzy numbers with integral value. Fuzzy Sets and Systems, 50(3), 247–255.Google Scholar
  10. Nedic, N., Stojanovic, V., & Djordjevic, V. (2015). Optimal control of hydraulically driven parallel robot platform based on firefly algorithm. Nonlinear Dynamics, 82(3), 1–17.Google Scholar
  11. Pavlyukevich, I. (2007). Lévy flights, non-local search and simulated annealing. Journal of Computational Physics, 226(2), 1830–1844.Google Scholar
  12. Prsic, D., Nedic, N., & Stojanovic, V. (2016). A nature inspired optimal control of pneumatic-driven parallel robot platform. Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, 231(1), 59–71.Google Scholar
  13. Stojanovic, V., & Nedic, N. (2016). A nature inspired parameter tuning approach to cascade control for hydraulically driven parallel robot platform. Journal of Optimization Theory and Applications, 168(1), 332–347.Google Scholar
  14. Stojanovic, V., Nedic, N., Prsic, D., Dubonjic, L., & Djordjevic, V. (2016). Application of cuckoo search algorithm to constrained control problem of a parallel robot platform. International Journal of Advanced Manufacturing Technology, 87, 1–11.Google Scholar
  15. Storn, R., & Price, K. (1997). Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.Google Scholar
  16. Tao, F., Zhao, D. M., Hu, Y. F., & Zhou, Z. D. (2008). Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. IEEE Transactions on Industrial Informatics, 4(4), 315–327.Google Scholar
  17. Tao, F., Zhao, D. M., Yefa, H., & Zhou, Z. D. (2010). Correlation-aware resource service composition and optimal-selection in manufacturing grid. European Journal of Operational Research, 201(1), 129–143.Google Scholar
  18. Van Laarhoven, P. J. M., & Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(3), 229–241.Google Scholar
  19. Wang, R., & Zhou, Y. Q. (2014). Flower pollination algorithm with dimension by dimension improvement. Mathematical Problems in Engineering, 2014(4), 1–9.Google Scholar
  20. Xiang, F., Hu, Y. F., Yu, Y. R., & Wu, H. C. (2014). QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system. Central European Journal of Operations Research, 22(4), 663–685.Google Scholar
  21. Xu, W. J., Tian, S. S., Liu, Q., Xie, Y. Q., Zhou, Z. D., & Pham, D. T. (2016). An improved discrete bees algorithm for correlation-aware service aggregation optimization in cloud manufacturing. The International Journal of Advanced Manufacturing Technology, 84(1), 17–28.Google Scholar
  22. Yang, X. S. (2012). Flower pollination algorithm for global optimization. In Processing of the 17th international conference on unconventional computing and natural computation, Orléans, France, pp. 240–249.Google Scholar
  23. Yang, X. S., Karamanoglu, M., & He, X. (2014). Flower pollination algorithm: A novel approach for multiobjective optimization. Engineering Optimization, 46(9), 194–195.Google Scholar
  24. Yaqiong, L., Man, L. K., & Zhang, W. (2011). Fuzzy theory applied in quality management of distributed manufacturing system: A literature review and classification. Engineering Applications of Artificial Intelligence, 24(2), 266–277.Google Scholar
  25. Yildiz, A. R. (2013). Optimization of multi-pass turning operations using hybrid teaching learning-based approach. International Journal of Advanced Manufacturing Technology, 66(9–12), 1319–1326.Google Scholar
  26. Yildiz, B. S. (2017a). A comparative investigation of eight recent population-based optimisation algorithms for mechanical and structural design problems. International Journal of Vehicle Design, 73(1–3), 208–218.Google Scholar
  27. Yildiz, B. S. (2017b). Natural frequency optimization of vehicle components using the interior search algorithm. Materialprufung, 59(5), 456–458.Google Scholar
  28. Yildiz, A. R., Kurtuluş, E., Demirci, E., Yildiz, B. S., & Karagöz, S. (2016a). Optimization of thin-wall structures using hybrid gravitational search and Nelder–Mead algorithm. Materialprufung, 58(1), 75–78.Google Scholar
  29. Yildiz, B. S., Lekesiz, H., & Yildiz, A. R. (2016b). Structural design of vehicle components using gravitational search and charged system search algorithms. Materialprufung, 58(1), 79–81.Google Scholar
  30. Yildiz, A. R., Pholdee, N., & Bureerat, S. (2017). Hybrid real-code population-based incremental learning and differential evolution for many-objective optimisation of an automotive floor-frame. International Journal of Vehicle Design, 73(1–3), 20–53.Google Scholar
  31. Yildiz, A. R., & Saitou, K. (2011). Topology synthesis of multicomponent structural assemblies in continuum domains. Journal of Mechanical Design, 133(1), 788–796.Google Scholar
  32. Yildiz, B. S., & Yildiz, A. R. (2017). Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes. Materialprufung, 59(5), 425–429.Google Scholar
  33. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.Google Scholar
  34. Zhang, W. Y., Yang, Y. S., Zhang, S., & Xu, Y. B. (2016a). A new manufacturing service selection and composition method using improved flower pollination algorithm. Mathematical Problems in Engineering, 2016(1), 1–12.Google Scholar
  35. Zhang, S., Yu, Z. N., Zhang, W. Y., Yu, D. J., & Xu, Y. B. (2016b). An extended genetic algorithm for distributed integration of fuzzy process planning and scheduling. Mathematical Problems in Engineering, 2016(3), 1–13.Google Scholar
  36. Zhang, W. Y., Zhang, S., Cai, M., & Huang, J. X. (2011). A new manufacturing resource allocation method for supply chain optimization using extended genetic algorithm. The International Journal of Advanced Manufacturing Technology, 53(53), 1247–1260.Google Scholar
  37. Zhang, W. Y., Zhang, S., Guo, S. S., Yang, Y. S., & Chen, Y. (2016c). Concurrent optimal allocation of distributed manufacturing resources using extended teaching-learning-based optimization. International Journal of Production Research, 55, 1–18.Google Scholar
  38. Zhou, Y. Q., Wang, R., & Luo, Q. F. (2016). Elite opposition-based flower pollination algorithm. Neurocomputing, 188, 294–310.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Shuai Zhang
    • 1
  • Yangbing Xu
    • 1
  • Wenyu Zhang
    • 1
    Email author
  • Dejian Yu
    • 1
  1. 1.School of InformationZhejiang University of Finance and EconomicsHangzhouChina

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