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Journal of Intelligent Manufacturing

, Volume 30, Issue 3, pp 1009–1019 | Cite as

Multi-agent system approach applied to a manufacturer’s supply chain using global objective function and learning concepts

  • Rafaella de Souza HenriquesEmail author
Article

Abstract

The supply chain efficiency is essential to guarantee the producer competitiveness. As this environment encompasses intelligent actors, it can be model as a multi-agent system. In order to evaluate the interactions, competition, strategies and their consequences in this context, this study aims to analyze the multi-agent system approach applied in a manufacturer’s supply chain, considering a pull production system. Then, this study starts with the literature review about multi-agent systems and its application to supply chain. After, the proposed system is explained, describing its architecture, organization, objectives, negotiation and learning aspects. All the assumptions, individual objective functions and global objective functions are presented in this section. Following, the interactions and implementations aspects are addressed. The negotiation design was inspired in the monotonic concession protocol and the Dutch auctions. The agents learning aspect is broached by the \(\upvarepsilon \)-greedy heuristic. Finally, the experiments design, results and conclusions are presented. The results show the agents’ behavior and the overall system evaluation.

Keywords

Multi-agent system Supply chain management Self-interested agents Pull production system 

References

  1. Arunachalam, R., & Sadeh, N. M. (2005). The supply chain trading agent competition. Electronic Commerce Research and Applications, 4, 66–84.CrossRefGoogle Scholar
  2. Giannakis, M., & Louis, M. (2011). A multi-agent based framework for supply chain risk management. Journal of Purchasing and Supply Management, 17, 23–31.CrossRefGoogle Scholar
  3. Handfield, R. B., & Nichols, E. L. (1999). Introduction to supply chain management. Upper Saddle River, NJ: Prentice Hall.Google Scholar
  4. Julka, N., Srinivasan, R., & Karimi, I. (2002). Agent-based supply chain management–1: framework. Computers and Chemical Engineering, 26, 1755–1769.CrossRefGoogle Scholar
  5. Kumar, V., & Srinivasan, S. (2010). A review of supply chain management using multi-agent system. International Journal of Computer Science, 7(5), 198–205.Google Scholar
  6. Lee, J. H., & Kim, C. O. (2008). Multi-agent systems applications in manufacturing systems and supply chain management: A review paper. International Journal of Production Research, 46(1), 233–265.CrossRefGoogle Scholar
  7. Leitão, P. (2009). Agent-based distributed manufacturing control: A state-of the-art survey. Engineering Applications of Artificial Intelligence, 22(7), 979–991.CrossRefGoogle Scholar
  8. Lou, P., Chen, Y. P., & Ai, W. (2004). Study on multi-agent-based agile supply chain management. The International Journal of Advanced Manufacturing Technology, 23(3–4), 197–203.CrossRefGoogle Scholar
  9. Roy, D., Anciaux, D., Monteiro, T., & Ouzizi, L. (2004). Multi-agent architecture for supply chain management. Journal of Manufacturing Technology Management, 15(8), 745–755.CrossRefGoogle Scholar
  10. Sabri, M., & Garakani, M. F. (2012). Modeling an agent mediated supply chain management system using MAS-commonKADS methodology. International Journal of Mechatronics, Electrical and Computer Technology, 2(3), 58–75.Google Scholar
  11. Sardinha, J. A. R. P., Molinaro, M. S., Paranhos, P. M., Cunha, P. M., Milidiú, R. L., & Lucena, C. J. P. (2006). A multi-agent architecture for a dynamic supply chain management. In Proceedings of the nineteenth international florida artificial intelligence research society conference (Vol. 1, pp. 178–179). American Association for Artificial Intelligence.Google Scholar
  12. Weiss, G. (1999). Multi-agent systems: A modern approach to distributed artificial intelligence. Cambridge: MIT Press.Google Scholar
  13. Wooldridge, M. (2009). An introduction to multi-agent systems. Hoboken: Wiley.Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Centro Federal de Educacao Tecnologica de Minas GeraisBelo HorizonteBrazil

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