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Combining Simulation and Multi-agent Systems for Solving Enterprise Process Flows Constraints in an Enterprise Modeling Aided Tool

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Part of the Communications in Computer and Information Science book series (CCIS,volume 430)

Abstract

European economies have been deeply affected by different crises. The impact of the economic crisis on enterprises is now recognized by everybody. Enterprises need to reorganize in order to be better adapted to this situation. GRAI Methodology is one of the three main methodologies for enterprise modeling. GRAIMOD is a software tool being developed for supporting this methodology and facilitating enterprise improvement. The concepts elaborated for this tool combine reasoning like Case Based Reasoning (CBR), Decomposition or transformation reasoning and multi-agent systems like training agent. This paper introduces these concepts and presents how to complete them with simulation concepts for improving enterprise performance. An example will be used for illustrating the concepts presented through a detailed case study.

Keywords

  • Multi-agent systems
  • Expert system
  • Simulation
  • Reference models
  • Rules
  • Knowledge

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  • DOI: 10.1007/978-3-319-07767-3_15
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References

  1. Aamodt, A.: Case-Based Reasoning: foundational issues, methodological variations, and system approaches. Artificial Intelligence Communications 7(1), 39–59 (1994)

    Google Scholar 

  2. 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 

  3. Brown, D.C., Chandrasekaran, B.: Expert system for a class of mechanical design activities. Knowledge Engineering in CAD. Elsevier, Amsterdam (1985)

    Google Scholar 

  4. Chen, D., Doumeingts, G., Vernadat, F.B.: Architectures for enterprise integration and interoperability. Past, present and future. Computers in Industry 59, 647–659 (2008)

    CrossRef  Google Scholar 

  5. Dossou, P.E., Mitchell, P.: Using case based reasoning in GRAIXPERT. In: FAIM 2006, Limerick, Ireland (2006)

    Google Scholar 

  6. Dossou, P.E., Mitchell, P.: Implication of Reasoning in GRAIXPERT for modeling Enterprises. In: DCAI 2009, Salamanca, Spain (2009)

    Google Scholar 

  7. Dossou, P.E., Mitchell, P.: How Quality Management could improve the Supply Chain performance of SMES. In: FAIM 2009, Middlesbrough, United Kingdom (2009)

    Google Scholar 

  8. Dossou, P.-E., Pawlewski, P.: Using multi-agent system for improving and implementing a new enterprise modeling tool. In: Demazeau, Y., et al. (eds.) Trends in PAAMS. AISC, vol. 71, pp. 225–232. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  9. European commission: Responsabilité sociale des entreprises: une nouvelle stratégie de l’UE pour la période 2011-2014, Brussels, Belgium (2011)

    Google Scholar 

  10. Ferber, J.: Multi-agent system: An Introduction to distributed Artificial Intelligence. Addison Wesley Longman, Harlow, ISBN 0-201-36048-9

    Google Scholar 

  11. Friedman-Hill, E.: JESS, the rule engine for the JAVA platform, version 7.1p2. Sandia National Laboratories (2008)

    Google Scholar 

  12. Russell, S.J., Norvig, P.: Artificial Intelligence. A Modern Approach. Prentice-Hall, Englewood Cliffs (1995)

    MATH  Google Scholar 

  13. Sen, S., Weiss, G.: Learning in Multiagent Systems. In: Weiss, G. (ed.) MultiagentSystems: A Modern Approach to Distributed Artificial Intelligence, ch. 6, pp. 259–298. The MIT Press, Cambridge (1999)

    Google Scholar 

  14. Sycara, K.P.: Multi-agent systems. AI Magasine, American Association for Artificial Intelligence (1998) 0738-4602-1998

    Google Scholar 

  15. 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 

  16. 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)

    CrossRef  Google Scholar 

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Dossou, PE., Pawlewski, P., Mitchell, P. (2014). Combining Simulation and Multi-agent Systems for Solving Enterprise Process Flows Constraints in an Enterprise Modeling Aided Tool. In: , et al. Highlights of Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection. PAAMS 2014. Communications in Computer and Information Science, vol 430. Springer, Cham. https://doi.org/10.1007/978-3-319-07767-3_15

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  • DOI: https://doi.org/10.1007/978-3-319-07767-3_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07766-6

  • Online ISBN: 978-3-319-07767-3

  • eBook Packages: Computer ScienceComputer Science (R0)