A Decisional Multi-Agent Framework for Automatic Supply Chain Arrangement

  • Luca Greco
  • Liliana Lo Presti
  • Agnese Augello
  • Giuseppe Lo Re
  • Marco La Cascia
  • Salvatore Gaglio
Part of the Studies in Computational Intelligence book series (SCI, volume 439)

Abstract

In this work, a multi-agent system (MAS) for supply chain dynamic configuration is proposed. The brain of each agent is composed of a Bayesian Decision Network (BDN); this choice allows the agent for taking the best decisions estimating benefits and potential risks of different strategies, analyzing and managing uncertain information about the collaborating companies. Each agent collects information about customer’s orders and current market prices, and analyzes previous experiences of collaborations with trading partners. The agent therefore performs a probabilistic inferential reasoning to filter information modeled in its knowledge base in order to achieve the best performance in the supply chain organization.

Keywords

Supply Chain Business Process Supply Chain Management Multiagent System Selling Price 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Klein, M.R., Methlie, L.B.: Knowledge-Based Decision Support Systems: with Applications in Business, 2nd edn. John Wiley and Sons, Inc. (1995)Google Scholar
  2. 2.
    Chan, H.K., Chan, F.T.S.: Comparative study of adaptability and flexibility in distributed manufacturing supply chains. Decision Support Systems 48(2), 331–341 (2010), ISSN 0167-9236, doi:10.1016/j.dss.2009.09.001CrossRefGoogle Scholar
  3. 3.
    Datta, P.P., Christopher, M.G.: Information sharing and coordination mechanisms for managing uncertainty in supply chains: a simulation study. International Journal of Production Research 49(3), 765–803 (2011)CrossRefGoogle Scholar
  4. 4.
    Kumar, V., Srinivasan, S.: A Review of Supply Chain Management using Multi-Agent System. International Journal of Computer Science Issues 7(5) (September 2010)Google Scholar
  5. 5.
    Sycara, K.P.: Multiagent systems. AI Magazine 19(2), 79–92 (1998)Google Scholar
  6. 6.
    Wooldridge, M.: Intelligent agents. In: Gerhard, W. (ed.) Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, ch. 1, pages 2778. The MIT Press (1999)Google Scholar
  7. 7.
    Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Pearson Education (2003)Google Scholar
  8. 8.
    A Multi-Agent Decision Support System for Dynamic Supply Chain Organization. In: Proceedings of the 5th International Workshop on New Challenges in Distributed Information Filtering and Retrieval (DART 2011), Palermo, Italy, September 17 (2011)Google Scholar
  9. 9.
    Jain, V., Wadhwa, S., Deshmukh, S.G.: Revisiting information systems to support a dynamic supply chain: issues and perspectives. Production Planning and Control: The Management of Operations 20(1), 17–29 (2009)CrossRefGoogle Scholar
  10. 10.
    Sadeh, N.M., Hildum, D.W., Kjenstad, D.: Agent-Based E-Supply Chain Decision Support. Journal of Organizational Computing and Electronic Commerce 13(3 and 4), 225–241 (2003)Google Scholar
  11. 11.
    Moyaux, T., Chaib-Draa, B.: Supply Chain Management and Multiagent Systems: An Overview. In: Chaib-Draa, B., Mller, J.P. (eds.) Multiagent-Based Supply Chain Management, pp. 1–27 (2006)Google Scholar
  12. 12.
    Collins, J., Ketter, W., Sadeh, N.: Pushing the limits of rational agents: the Trading Agent Competition for Supply Chain Management. AI Magazine 31(2) (Summer 2010); Also available as Technical Report CMU-ISR-09-129Google Scholar
  13. 13.
    Zhang, Z., Tao, L.: Multi-agent Based Supply Chain Management with Dynamic Reconfiguration Capability. In: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2008), vol. 02, pp. 92–95. IEEE Computer Society, Washington, DC (2008), http://dx.doi.org/10.1109/WIIAT.2008.276, doi:10.1109/WIIAT.2008.276CrossRefGoogle Scholar
  14. 14.
    Piramuthu, S.: Machine learning for dynamic multi-product supply chain formation. Expert Systems with Applications 29(4), 985–990 (2005) ISSN: 0957-4174, doi:10.1016/j.eswa.2005.07.004CrossRefGoogle Scholar
  15. 15.
    Guneri, A.F., Yucel, A., Ayyildiz, G.: An integrated fuzzy-lp approach for a supplier selection problem in supply chain management. Expert Systems with Applications 36, 9223–9228 (2009)CrossRefGoogle Scholar
  16. 16.
    Smith, R.G.: The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on Computers 100(12), 1104–1113 (1980)CrossRefGoogle Scholar
  17. 17.
    Hsieh, F.-S.: Analysis of contract net in multi-agent systems. Automatica 42(5), 733–740 (2006) ISSN 00051098MathSciNetMATHCrossRefGoogle Scholar
  18. 18.
    Wu, B., Cheng, T., Yang, S., Zhang, Z.: Price-based negotiation for task assignment in a distributed network manufacturing mode environment. The International Journal of Advanced Manufacturing Technology 21(2), 145–156 (2003)Google Scholar
  19. 19.
    Van Brussel, H., Wyns, J., Valckenaers, P., Bongaerts, L., Peeters, P.: Reference architecture for holonic manufacturing systems: PROSA. Computers in Industry 37(3), 255–274 (1998)CrossRefGoogle Scholar
  20. 20.
    Alibhai, Z.: What is Contract Net Interaction Protocol? IRMS Lab. SFU (July 2003)Google Scholar
  21. 21.
    Lam, K.-C., Tao, R., La, M.C.-K.: A materialsupplier selection model for property developers using Fuzzy Principal Component Analysis. Automation in Construction 19, 608–618 (2010)CrossRefGoogle Scholar
  22. 22.
    Chen, Y., Peng, Y.: An Extended Bayesian Belief Network Model of Multi-agent Systems for Supply Chain Managements. In: Truszkowski, W., Hinchey, M., Rouff, C.A. (eds.) WRAC 2002. LNCS (LNAI), vol. 2564, pp. 335–346. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  23. 23.
  24. 24.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Luca Greco
    • 1
  • Liliana Lo Presti
    • 1
  • Agnese Augello
    • 2
  • Giuseppe Lo Re
    • 1
  • Marco La Cascia
    • 1
  • Salvatore Gaglio
    • 1
  1. 1.DICGIM, University of PalermoPalermoItaly
  2. 2.ICAR Italian National Research CouncilPalermoItaly

Personalised recommendations