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Fuzzy Decision-Making Method for Product Holons Encountered Emergency Breakdown in Product-Driven System: An Industrial Case

  • Ming Li
  • Hind Bril El HaouziEmail author
  • André Thomas
  • Arnould Guidat
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 594)

Abstract

In this paper a fuzzy decision-making method is proposed to make local decisions in case of breakdown occurring in a context of product-driven systems. To cope with breakdown uncertainty, three parameters, α, β and γ are created to evaluate the impact of it. Further, a fuzzy rule based on a membership function is designed to switch between centralized and distributed decisions concerning the re-arranging of the remaining parts. Simulation results show that appropriate decisions could be made by the proposed fuzzy decision-making method with certain suitable parameters. This method was applied on an existing industrial case; it can be easily extended to make decision for breakdown events in other contexts.

Keywords

Fuzzy decision-making method Product-driven system Breakdown Holonic manufacturing systems 

References

  1. 1.
    Borangiu, T., Gilbert, P., Ivanescu, N.A., et al.: An implementing framework for holonic manufacturing control with multiple robot-vision stations. Eng. Appl. Artif. Intell. 22, 505–521 (2009)CrossRefGoogle Scholar
  2. 2.
    Borangiu, T.: A service-orientated architecture for holonic manufacturing control. Stud. Comput. Intell. 243, 489–503 (2009)CrossRefGoogle Scholar
  3. 3.
    Cossentino, M., Gaud, N., Galland, S., et al.: A holonic metamodel for agent-oriented analysis and design. Lect. Notes Comput. Sci. 4659, 237–246 (2007)Google Scholar
  4. 4.
    El Haouzi, H., Petin, J.F., Thomas, A.: Design and validation of a product-driven control system based on a six sigma methodology and discrete event simulation. Prod. Plann. Control 20(6), 510–524 (2009) Google Scholar
  5. 5.
    Herrera, C., Berraf, S.B., Thomas, A.: Viable system model approach for holonic product driven manufacturing systems. Service Orientation in Holonic and Multi-agent Manufacturing Control, pp. 169–181. Springer, Berlin (2012)CrossRefGoogle Scholar
  6. 6.
    McFarlane, D., Sarma, S., Chirn, J.L., et al.: Auto ID systems and intelligent manufacturing control. Eng. Appl. Artif. Intell. 16(4), 365–376 (2003)CrossRefGoogle Scholar
  7. 7.
    Meyer, G.G., Roest, G.B., Szirbik, N.B. Intelligent products for monitoring and control of road-based logistics. In: 2010 International Conference on Management and Service Science (MASS), pp. 1–6, IEEE (2010)Google Scholar
  8. 8.
    Meyer, G.G., Wortmann, J.C., Szirbik, N.B.: Production monitoring and control with intelligent products. Int. J. Prod. Res. 49(5), 1303–1317 (2011)CrossRefGoogle Scholar
  9. 9.
    Morel, G., Valckenaers, P., Faure, J.M., Pereira, C.E., Diedrich, C.: Manufacturing plant control challenges and issues. Control Eng. Pract. 15(11), 1321–1331 (2007)CrossRefGoogle Scholar
  10. 10.
    Pannequin, R., Morel, G., Thomas, A.: The performance of product-driven manufacturing control: an emulation-based benchmarking study. Comput. Ind. 60(3), 195–203 (2009)CrossRefGoogle Scholar
  11. 11.
    Pannequin, R., Thomas, A.: Another interpretation of stigmergy for product-driven systems architecture. J. Intell. Manuf. 23(6), 2587–2599 (2012)CrossRefGoogle Scholar
  12. 12.
    Sallez, Y., Berger, T., Trentesaux, D.: A stigmergic approach for dynamic routing of active products in FMS. Comput. Ind. 60(3), 204–216 (2009)CrossRefGoogle Scholar
  13. 13.
    Shen, W., Norrie, D.H.: Agent-based systems for intelligent manufacturing: a state-of-the-art survey. Knowl. Inf. Syst. 1(2), 129–156 (1999)CrossRefGoogle Scholar
  14. 14.
    Shi, K., Chen, X., Qin, X.: Pheromone based self-routing of intelligent products in dynamic manufacturing system. Applied Informatics and Communication, pp. 260–268. Springer, Berlin (2011)CrossRefGoogle Scholar
  15. 15.
    Valckenaers, P., Brussel, H.V.: Holonic manufacturing execution systems. CIRP Ann. Manuf. Technol. 54(1), 427–432 (2005)CrossRefGoogle Scholar
  16. 16.
    Van Brussel, H., Wyns, J., Valckenaers, P., et al.: Reference architecture for holonic manufacturing systems: PROSA. Comput. Ind. 37(3), 255–274 (1998)CrossRefGoogle Scholar
  17. 17.
    Vrba, P., Macurek, F., Mank, V.: Using radio frequency identification in agent-based control systems for industrial applications. Eng. Appl. Artif. Intell. 21(3), 331–342 (2008)CrossRefGoogle Scholar
  18. 18.
    Wong, C.Y., McFarlane, D., Ahmad Zaharudin, A. et al. The intelligent product driven supply chain. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 6–11, IEEE (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ming Li
    • 1
    • 2
  • Hind Bril El Haouzi
    • 2
    Email author
  • André Thomas
    • 2
  • Arnould Guidat
    • 2
  1. 1.Southwest Forestry UniversityKunmingChina
  2. 2.Research Centre for Automatic Control (CRAN), CNRS (UMR 7029)Nancy University, ENSTIBEpinalFrance

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