Advertisement

ETOMA: A Multi-Agent Tool for Shop Scheduling Problems

  • S. Bouzidi-Hassini
  • S. Bourahla
  • Y. Saboun
  • F. Benbouzid-Sitayeb
  • S. Khelifati
Part of the Studies in Computational Intelligence book series (SCI, volume 551)

Abstract

In this paper, we present ETOMA a multi-agent framework dedicated to developing and testing floor shop production schedules. It is applied for both production and joint production and maintenance scheduling as well. ETOMA architecture is composed of three modules: Develop, Test and Blackboard. The first one defines all agents that model the floor shop and their behavior. The second one takes care of testing the scheduling solution by using Taillard benchmarks depending on floor shop types. The considered types are Flow-shop, Job-shop and Open-shop. Finally, the Blackboard insures communication between the two predefined modules. ETOMA allows developing and testing any scheduling solution without imposing a specific architecture for agents. Moreover, ETOMA provides at the end of simulations a report composed of a recapitulative table or a curve according to user choice.

Keywords

Multi-Agent Framework Simulation Scheduling Production Maintenance Taillard Benchmarks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pinedo, M.: Scheduling, Theory, Algorithms and Systems. Industrial and Systems Engineering. Edition Prentice Hall International (1995)Google Scholar
  2. 2.
    Li, X., Zhang, C., Gao, L., Li, W., Shao, X.: An agent-based approach for integrated process planning and scheduling. Journal of Expert Systems with Applications 37, 1256–1264 (2010)CrossRefGoogle Scholar
  3. 3.
    Kouiss, K., Pierreval, H., Mebarki, N.: Using multi-agent architecture in FMS for dynamic scheduling. Journal of Intelligent Manufacturing 8, 41–47 (1997)CrossRefGoogle Scholar
  4. 4.
    Owliya, M., Saadat, M., Goharian, M., Anane, R.: Agents-based Interaction Protocols and Topologies in Manufacturing Task Allocation. In: Proceeding of the 5th International Conference on System of Systems Engineering (2010)Google Scholar
  5. 5.
    Jackson, N., Lund, H.: Benchmarking for Higher Education. Society for Research into Higher Education & Open University Press, Buckingham (2000)Google Scholar
  6. 6.
    JADE, Java Agent DEvelopment Framework, http://jade.tilab.com
  7. 7.
  8. 8.
    Magique, Multi-agent hiérarchique, http://www.lifl.fr/MAGIQUE/presentation/index.html
  9. 9.
    FIPA, The Foundation for Intelligent Physical Agents, http://www.fipa.org
  10. 10.
    Mathieu, P., Routier, J.C., Secq, Y.: Multi-Agent hiérarchique, http://www2.lifl.fr/SMAC/projects/magique/presentation/presentationContent.html#intro
  11. 11.
    Coudert, T., Grabot, B., Archimède, B.: Système multi agents et logique floue pour un ordonnancement coopératif production/ maintenance. Journal of Decision Systems 13(1), 27–62 (2004)CrossRefGoogle Scholar
  12. 12.
    Cavory, G., Dupas, R., Goncalves, G.: A genetic approach to the scheduling of preventive maintenance tasks on a single product manufacturing production line. Int. J. Production Economics 74, 135–146 (2001)CrossRefGoogle Scholar
  13. 13.
    Taillard, E.: Benchmarks for basic scheduling problems. European Journal of Operational Research 64(2), 278–285 (1993)CrossRefMATHGoogle Scholar
  14. 14.
    Masc platform, http://www.irit.fr/MASC

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • S. Bouzidi-Hassini
    • 1
  • S. Bourahla
    • 1
  • Y. Saboun
    • 1
  • F. Benbouzid-Sitayeb
    • 1
  • S. Khelifati
    • 1
  1. 1.Laboratoire LMCSEcole nationale Supérieure d’informatiqueAlgerAlgérie

Personalised recommendations