Advertisement

A Volatile Knowledge Approach to Improve the Autonomy of Holons: Application to a Flexible Job Shop Manufacturing System

  • Emmanuel AdamEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9266)

Abstract

It is well known now that MAS are particularly adapted to deal with distributed and dynamic environment. The management of business workflow, or data flow, flexible job shop manufacturing systems is typically a good application field for them. This kind of application requires flexibility to face with changes on the network. In the context of FMS, where products and resources entities can be seen as active, and subject to events, a volatile knowledge concept has been defined. We illustrate our proposition on an emulator of the flexible assembly cell in our university.

Keywords

Volatile knowledge Flexible job shop manufacturing systems Multiagent system 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adam, E., Berger, T., Sallez, Y., Trentesaux, D.: An Open-Control Concept for a Holonic Multiagent System. In: Mař\’ık, V., Strasser, T., Zoitl, A. (eds.) HoloMAS 2009. LNCS, vol. 5696, pp. 145–154. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  2. 2.
    Adam, E., Grislin, E., Mandiau, R.: Autonomous Agents in Dynamic Environment: A Necessary Volatility of the Knowledge. In: Bajo Perez, J., et al. (eds.) Trends in Practical Applications of Heterogeneous Multi-agent Systems. The PAAMS Collection. AISC, vol. 293, pp. 103–110. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  3. 3.
    Brandimarte, P.: Routing and scheduling in a flexible job shop by tabu search. Ann. Oper. Res. 41(1–4), 157–183 (1993)CrossRefzbMATHGoogle Scholar
  4. 4.
    Conway, R., Maxwell, W., Miller, L.: Theory of scheduling. Dover Books on Computer Science, Dover Publications (Reprint edn.) (2003)Google Scholar
  5. 5.
    Dorigo, M., Blum, C.: Ant colony optimization theory: A survey. Theor. Comput. Sci. 344(2–3), 243–278 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Havens, T., Spain, C., Salmon, N., Keller, J.: Roach infestation optimization. In: Swarm Intelligence Symposium, SIS 2008, pp. 1–7. IEEE (September 2008)Google Scholar
  7. 7.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Neural Networks, vol. 4, pp. 1942–1948. IEEE (November 1995)Google Scholar
  8. 8.
    Ketenci, U., Adam, E., Grislin, E., Mandiau, R.: Volatile knowledge for mobile agents : application to autonomous vehicles management. In: 11th European Workshop on Multi-Agent Systems (EUMAS 2013), Toulouse, France (2013) (short paper)Google Scholar
  9. 9.
    Lin, F., Reiter, R.: Forget it! In: Proceedings of the AAAI Fall Symposium on Relevance, pp. 154–159 (1994)Google Scholar
  10. 10.
    Teodorović, D.: Bee Colony Optimization (BCO). In: Lim, C.P., Jain, L.C., Dehuri, S. (eds.) Innovations in Swarm Intelligence. SCI, vol. 248, pp. 39–60. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  11. 11.
    Trentesaux, D., Pach, C., Bekrar, A., Sallez, Y., Berger, T., Bonte, T., Leitão, P., Barbosa, J.: Benchmarking flexible job-shop scheduling and control systems. Control Engineering Practice 21(9), 1204–1225 (2013)CrossRefGoogle Scholar
  12. 12.
    Vakil Baghmisheh, M., Madani, K., Navarbaf, A.: A discrete shuffled frog optimization algorithm. Artificial Intelligence Review 36(4), 267–284 (2011)CrossRefGoogle Scholar
  13. 13.
    Yang, X.S., He, X.: Firefly algorithm: recent advances and applications. International Journal of Swarm Intelligence 1(1), 36–50 (2013)CrossRefGoogle Scholar
  14. 14.
    Yen, G.G., Hickey, T.W.: Reinforcement learning algorithms for robotic navigation in dynamic environments. ISA Transactions 43(2), 217–230 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.UVHC, LAMIH Lab., UMR CNRS 8201ValenciennesFrance

Personalised recommendations