Skip to main content
Book cover

International Conference on Practical Applications of Agents and Multi-Agent Systems

PAAMS 2014: Highlights of Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection pp 156–166Cite as

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

  • Conference paper

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

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-07767-3_15
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   69.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-07767-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   89.99
Price excludes VAT (USA)
Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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 

Download references

Author information

Authors and Affiliations

  1. ICAM, Site de Vendée, 28 Boulevard d’Angleterre, 85000, La Roche-Sur-Yon, France

    Paul-Eric Dossou & Philip Mitchell

  2. Poznan University of Technology, ul.Strzelecka 11, 60-965, Poznań, Poland

    Pawel Pawlewski

Authors
  1. Paul-Eric Dossou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pawel Pawlewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Philip Mitchell
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Departamento Informática y Automática, Universidad de Salamanca, Plaza de la Merced s/n, 37008, Salamanca, Spain

    Juan M. Corchado

  2. Department of Artificial Intelligence, Technical Univesity of Madrid, Bloque 2, Despacho 2101, Campus Montegancedo, Boadilla del Monte,, Madrid, Spain

    Javier Bajo

  3. AGH University of Science and Technology (AGH), 30-059, Krakow, Poland

    Jaroslaw Kozlak

  4. Poznan University of Technology, ul.Strzelecka 11, 60-965, Poznań, Poland

    Pawel Pawlewski

  5. Departamento de Informática, Universidad Carlos III de Madrid, Avenida de la Universidad Carlos III, 22, Colmenarejo, Madrid, Spain

    Jose M. Molina

  6. IRIT, University of Toulouse, 118 Route de Narbonne, 31062, Toulouse cedex 9, France

    Benoit Gaudou

  7. Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Camino de Vera s/n, 46022, Valencia, Spain

    Vicente Julian

  8. Universität Duisburg-Essen, Schützenbahn 70 (Hochhaus), 45117, Essen, Germany

    Rainer Unland

  9. Laboratório Nacional de Energia e Geologia (LNEG), Est. Paço do Lumiar 22, 1649-038, Lisbon, Portugal

    Fernando Lopes

  10. Maersk Mc-Kinney Moller Institute, University of Southern Denmark, DK-5230, Odense M, Denmark

    Kasper Hallenborg

  11. Dpt. Signal Theory, Telematics and Com-munications, University of Granada, C/ Periodista Daniel Saucedo Aranda, s/n, 18071, Granada, Spain

    Pedro García Teodoro

Rights and permissions

Reprints and Permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

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

Download citation

  • 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)