Comparison of Enterprise Integration Modelling Concepts Based on Intelligent Multi-Agent System

  • Paul Eric Dossou
  • Paweł Pawlewski
  • Philip Mitchell
Part of the Studies in Computational Intelligence book series (SCI, volume 422)

Abstract

CIMOSA and GRAI methodologies are mainly used for enterprise modeling. Two supporting tools, respectively VLPROGRAPH AND GRAIMOD, are developed for supporting these methodologies by using multiagent concepts. The architecture of VLPRO-GRAPH (Very Long Process Graph) is based on the assumption that the system will support the MPS creation in the ERP system and will be plugged in to the ERP system database by, for example java connector. The concepts of GRAIMOD are detailed. This tool combines the theory of Multi-Agent systems, Artificial Intelligence and Metaphors of Mind. The tools developed are compared in order to defined opportunities for future research. Both tools are developed by using Java language and Jade platform.

Keywords

Supply Chain Production Planning Multiagent System Case Base Reasoning Supply Chain Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Aamodt, A.: Case-Based Reasoning: foundational issues, methodological variations, and system approaches. Artificial Intelligence Communications 7(1), 39–59 (1994)Google Scholar
  2. 2.
    APICS: Dictionary, 12th edn. America Production and inventory Control Society, Inc., Falls Church (2008)Google Scholar
  3. 3.
    Boszko, J.: Factory Organization Structure and Optimization. WNT, Warszawa (1973) (in Polish)Google Scholar
  4. 4.
    Brown, D.C., Chandrasekaran, B.: Expert system for a class of mechanical design activities. In: Gero, J.S. (ed.) Knowledge Engineering in CAD, pp. 259–282. Elsevier, Amsterdam (1985)Google Scholar
  5. 5.
    Burke, E.K., et al.: Structured cases in case-based reasoning reusing and adapting cases for time-tabling problems. The Journal of KBS 13(2-3), 159–165 (2000)Google Scholar
  6. 6.
    Chen, D., Doumeingts, G., Vernadat, F.B.: Architectures for enterprise integration and interoperability. Past, present and future. Computers in Industry 59, 647–659 (2008)CrossRefGoogle Scholar
  7. 7.
    Dossou, P.E., Mitchell, P.: Using case based reasoning in GRAIXPERT. In: Proceedings of FAIM 2006, Limerick, Ireland (2006)Google Scholar
  8. 8.
    Dossou, P.E., Mitchell, P.: Implication of Reasoning in GRAIXPERT for modeling Enterprises. In: Omatuet, S., et al. (eds.) Distributed Computing Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, pp. 374–381. Springer, Heidelberg (2009)Google Scholar
  9. 9.
    Dossou, P.E., Mitchell, P.: How Quality Management could improve the Supply Chain performance of SMES. In: Proceedings of FAIM 2009, Middlesbrough, United Kingdom (2009)Google Scholar
  10. 10.
    Dossou, P.E., Pawlewski, P.: Using Multi-agent system for improving and implementing a new enterprise modeling tool. In: Damazeu, Y. (ed.) Trends in Practical Applications of Agents and Multiagents Systems, pp. 225–232. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Karageorgos, A., Mehandjiev, N., Weichhart, G., Hammerle, A.: Agent based optimisation of logistics and production planning. Engineering Application of Artificial Intelligencel 16, 335–348 (2003)CrossRefGoogle Scholar
  12. 12.
    Pawlewski, P., Goliñska, P., Fertsch, M., Tujillo, J., Pasek, Z.: Multiagent approach for supply chain integration by distributed production planning, scheduling and control system. In: Corchado, J.M., et al. (eds.) International Symposium on Distributed Computing and Artificial Intelligence (DCAI 2008). Advances in Soft Computing, pp. 29–37. Springer (2008)Google Scholar
  13. 13.
    Russels, S.J., Norvig, P.: Artificial Intelligence. A Modern Approach. Prentice-Hall, Englewood Cliffs (1995)Google Scholar
  14. 14.
    Sen, S., Weiss, G.: Learning in Multiagent Systems. In: Weiss, G. (ed.) Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, ch. 6, pp. 259–298. The MIT Press, Cambridge (1999)Google Scholar
  15. 15.
    Shen, W., Wang, L., Hao, Q.: Agent-Based Distributed Manufacturing Process Planning and Scheduling: A state-of-art survey. IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and Reviews 36(4), 563–577 (2006)CrossRefGoogle Scholar
  16. 16.
    Wooldridge, M.: Intelligent Agents. In: Weiss, G. (ed.) Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, ch. 1, pp. 27–77. The MIT Press, Cambridge (1999)Google Scholar
  17. 17.
    Xia, Q., et al.: Knowledge architecture and system design for intelligent operation support systems. The Journal Expert Systems with Applications 17(2), 115–127 (1999)CrossRefGoogle Scholar
  18. 18.
    Zaborowski, M.: Enterprise Resources The Follow-up Control (in Polish). Wydawnictwo Pracowni Komputerowej Jacka Skalmierskiego, Gliwice (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Paul Eric Dossou
    • 1
  • Paweł Pawlewski
    • 2
  • Philip Mitchell
    • 1
  1. 1.Icam Group, Icam VendeeLa Roche-Sur-YonFrance
  2. 2.Poznań University of TechnologyPoznańPoland

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