Abstract
The main concern of this research work is to analyse and model supply chains (SCs) in the particular context of small and medium enterprises (SMEs) in the field of mechatronic. The study is based on the analysis of the organisational features, the actors’ behaviour, and performance considerations. The development of the model relies on an iterative framework that progressively integrates different aspects into the model. This framework is the ArchMDE process, which is based on MDE (Model Driven Engineering). A major feature of this work lies in its contribution to two different areas of research. The first contribution of the work is to propose a generic metamodel for SCs. Based on a literature review, an incremental framework is proposed for the modelling of SCs in terms of concepts, structure and relationships. The application of the framework to the studied context is described and its result is a domain-metamodel for SCs. The second contribution of this work lies in the formalisation of the dynamic behaviour of the concepts in the metamodel. This formalisation is based on the multi-agent approach. An agentification of the metamodel is thus drawn, thanks to the natural links between multiagent theory and SC reality. This step leads to an agentified-domain-metamodel which also includes the monitoring of the SC and synchronisation protocols. By adding relationships and dynamic behavior aspects, we obtain a metamodel of the domain that can be implemented, with its static and dynamic aspects. To validate this model, an industrial case study is detailed and has been instantiated and encoded in JAVA.
Similar content being viewed by others
References
Al-Mutawah K., Lee V., Cheung Y. (2009) A new multi-agent system framework for tacit knowledge management in manufacturing supply chains. Journal of Intelligent Manufacturing 20(5): 593–610
Azaiez, S. (2007). Approche Dirigée par les modèles pour le développement de systèmes multi-agents. Thèse de l’Université de Savoie, Spécialité Informatique. Annecy le vieux, France.
Azaiez, S., Habchi, G., Huget, M. P., Pralus, M., & Tounsi, J. (2007). Multiagent oriented modelling and simulation for manufacturing systems control. In INDIN 2007, 5th IEEE international conference on industrial informatics (pp. 23–27). Vienna, Austria.
Bagchi, S., Buckley, S., Ettl, M., & Lin, G. (1998). Experience using the IBM supply chain simulator. Winter Simulation Conference.
Beamon B. M. (1998) Supply chain designs and analysis: Models and methods. International Journal of Production Economics 55: 281–294
Berger T., Sallez Y., Valti B., Gibaud A., Trentesaux A. (2010) Semi-heterarchical allocation and routing processes in FMS control: A stigmergic approach. Journal of Intelligent & Robotics Systems 58(1): 17–45
Bratman M. E, Israel D. J., Pollack M. (1998) Plans and resource-bounded practical reasoning. Computational Intelligence 4: 349–355
Brooks R. A. (1991) Intelligence without representation. Artificial Intelligence 47: 139–160
Chopra S., Meindl P. (2001) Supply chain management: Strategy planning and operation. Prentice-hall, Upper Saddle River, NJ
Cooper M., Lambert D. M., Pagh J. D. (1997) Supply chain management: More than a new name for logistics. International Journal of Logistics Management 18(2): 1–13
Demazeau, Y. (1996). Vowels, Invited lecture, IWDAIMAS96.
Drucker P.F. (1998) Management’s new paradigms. Forbes Magazine, 162(7): 152–177
Fischer, K., Müller, J. P., & Pischel, M. (1995). Unifying control in layered agent architecture. In IJCAI95, agent theory, architecture and language workshop (pp. 240–252).
Frayret J. M, D’amours S., Rousseau A., Harvey S., Gaudreault J. (2008) Agent-based supply chain planning in the forest products industry. International Journal of Flexible Manufacturing Systems 19(4): 358–391
Fung R. Y. K., Chen T. (2005) A multiagent supply chain planning and coordination architecture. The International Journal of Advanced Manufacturing Technology 25(7–8): 811–819
Gutknecht, O., Ferber, J., & Michel, F. (2000). MadKit: Une expérience d’architecture de plate-forme multi-agents générique. 8ème Journées Francophones Intelligence Artificielle Distribuée Systèmes Multi-Agents JFIADSMA 2000. Saint-Jean Le Vêtre Octobre.
Ingalls, R. G. (1998). The value of simulation in modeling supply chain. In Proceedings of the 1998 winter simulation conference (pp. 1371–1375). Washington DC.
Ito T., Abadi S. M. M. J. (2002) Agent-based material handling and inventory planning in warehouse. Journal of Intelligent Manufacturing 13(3): 201–210
Janssen, M. (2005). The architecture and business value of a semi-cooperative, agent-based supply chain management system. Electronic Commerce Research and Applications (4),315–328.
Julien P.A. (1997) Les PME bilan et perspectives 2e edition. Paris, France, Economica
Kent, S. (2002). Model-driven engineering. In IFM 2002, Vol. 2335 of LNCS (pp. 286–298). Springer-Verlag.
Labarthe O., Espinasse B., Ferrarini A., Montreuil B. (2007) Toward a methodological framework for agent-based modeling and simulation of supply chains in a mass customization context. Simulation Modeling Practice and Theory 15: 113–136
Lambert D. M., Cooper M. C. (2000) Issues in supply chain management. Industrial Marketing Management 29(1): 65–83
Lee Y. H, Cho M. K., Kim S. J., Kim Y. B. (2002) Supply chain simulation with discrete continuous combined modelling. Computer and Industrial Engineering 43: 375–392
Longo F., Mirabelli G. (2008) An advanced supply chain management tool based on modelling and simulation. Computers & Industrial Engineering 54: 570–588
Monostori T., Vancza J., Kumara S. R. T. (2006) Agent-based systems for manufacturing. Annals of the CIRP 55(2): 697–720
Monteiro, T., Anciaux, D., D’amours, S., Espinasse, B., Ferrarini, A., & Labarthe, O. (2008). Simulation à base d’agents des systèmes de coordination et de planification des réseaux d’entreprises. In Chapitre 7 de : La simulation pour la gestion des chaînes logistiques, Lavoisier, Hermes Science Publications.
Oztemel E., Tekez E.K. (2009) Interaction of agents in performance based supply chain management. Journal of Intelligent Manufacturing, 20(2): 159–167
Parunak, H., Savit, R., & Riolo, R. L. (1998). Agent-based modelling VS equation-based modelling: A case study and user’s guide. In Proceedings of multi-agent systems and agent-based simulation (MABS’98), LNAI 1534. Springer.
Parunak, H. (1998). Wat can agents do in industry, and why? An overview of industrially-oriented R&D at CEC. Second international workshop on cooperative information agents, CIA’98. M. Klusch.
Parunak H.V.D. (1999) Industrial and practical applications of DAI. In: Weiss G. (eds) Multi-agents systems. MIT Press, Cambridge, MA, pp 377–421
Parunak, H., Savit, R., Riolo, R., & Stevens, J. (1999). DASCh: Dynamic analysis of supply chains. In Center of electronic commerce, DASCh final Report, part 0: Executive summary.
Sarimveis H., Patrinos P., Tarantilis C. D., Kiranoudis C. T. (2008) Dynamic modelling and control of supply chain systems: A review. Computers & Operations Research 35: 3530–3561
Shen W., Norrie D. H. (1999) Agent-based systems for intelligent manufacturing : A state-of-the-art survey. Knowledge and Information Systems, an International Journal 1(2): 129– 156
Shen W., Hao Q., Yoon H. J, Norrie D. H. (2006) Applications of agent-based systems in intelligent manufacturing: An updated review. Advanced Engineering Informatics 20: 415–431
Stevens G. C. (1989) Integrating the supply chain. International Journal of Physical Distribution and Materials Management 19(8): 3–8
Terzi S., Cavalieri S. (2004) Simulation in the supply chain context: A survey. Computers in Industry 53: 3–16
Thierry, C. (2003). Gestion des chaînes logistiques : Modèle et mise en œuvre pour l’aide à la décision à moyen terme, Accreditation to supervise research. University of Toulouse II.
Tounsi, J., Boissière, J., & Habchi, G. (2008). A conceptual model for SME mechatronics supply chain. In 6th international industrial simulation conference (ISC’08) (pp. 273–280). Lyon, France.
Tounsi, J. (2009). Modélisation pour la simulation de la chaîne logistique globale dans un environnement de production PME mécatronique. PhD Thesis of The University of Savoie, Industrial Engineering, 4th of December 2009.
Tounsi, J., Azaiez, S., Habchi, G., & Boissière, J. (2009c). A multiagent approach for modelling SMEs mechatronic supply chains. In Proceedings of the 13th IFAC symposium on information control problems in manufacturing.
Tounsi, J., Boissière, J., & Habchi, G. (2009a). Multiagent decision making for SME supply chain simulation. In Proceedings of 23rd European conference on modeling and simulation (ECMS) (pp. 203–211). Madrid, Espagne.
Tounsi, J., Habchi, G., & Boissière, J. (2009b). A multiagent system for production synchronization in SME mechatronic supply chain. In Proceedings of the 10th middle Eastern simulation and modeling conference (MESM) (pp. 91–97). Beirut, Liban.
Tounsi J., Habchi G., Boissière J. (2010) A conceptual model for SMEs mechatronics supply chain. International Journal of Computer Aided Engineering and Technology (IJCAET), 2(4): 371–387
Villarreal Lizarraga, C. L., Dupont, L., Gourg, D., & Pingaud, H. (2005). Contributing to management of shared projects in SMEs manufacturing clusters. In 18th international conference on production research (ICPR-18). Salerno, Italy.
Wooldridge M. (2002) An introduction to multiagent systems. Jonh Wiley & Sons, Chicester, UK
Wooldridge M. (1999) Intelligent agent. In: Weiss G. (eds) Multi-agents systems. MIT Press, Cambridge, MA, pp 27–77
Yuan, Y., Liang, T., & Zhang, J. (2001). Using agent technology to support supply chain management: Potentials and challenges. Michael G. DeGroote School of Business, McMaster University, Hamilton, Ontarioa, Canada, Working Paper No. 453.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tounsi, J., Habchi, G., Boissière, J. et al. A multi-agent knowledge model for SMEs mechatronic supply chains. J Intell Manuf 23, 2647–2665 (2012). https://doi.org/10.1007/s10845-011-0537-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-011-0537-1