Designing and Evaluating an Adaptive Trading Agent for Supply Chain Management

  • Minghua He
  • Alex Rogers
  • Esther David
  • Nicholas R. Jennings
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3937)


This paper describes the design and evaluation of SouthamptonSCM, a finalist in the 2004 International Trading Agent Supply Chain Management Competition (TAC SCM). In particular, we focus on the way in which our agent sets its prices according to the prevailing market situation and its own inventory level (because this adaptivity and flexibility are the key components of its success). Specifically, we analyse our pricing model’s performance both in the actual competition and in controlled experiments. Through this evaluation, we show that SouthamptonSCM performs well across a broad range of environments.


Inventory Level Reference Price Delivery Date Customer Order Bidding Strategy 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Collins, J., Arunachalam, R., et al.: The supply chain management game for the 2005 trading agent competition. Technical Report CMU-ISRI-04-139, School of Computer Science, Carnegie Mellon University (December 2004)Google Scholar
  2. 2.
    Dahlgren, E., Wurman, P.R.: PackaTAC: A conservative trading agent. SIGecom Exchanges 4(3), 33–40 (2004)CrossRefGoogle Scholar
  3. 3.
    He, M., Jennings, N.R.: Designing a successful trading agent: A fuzzy set approach. IEEE Transactions on Fuzzy Systems 12(3), 389–410 (2004)CrossRefGoogle Scholar
  4. 4.
    He, M., Jennings, N.R., Leung, H.F.: On agent-mediated electronic commerce. IEEE Transactions on Knowledge and Data Engineering 15(4), 985–1003 (2003)CrossRefGoogle Scholar
  5. 5.
    He, M., Leung, H.F., Jennings, N.R.: A fuzzy logic based bidding strategy for autonomous agents in continuous double auctions. IEEE Transactions on Knowledge and Data Engineering 15(6), 1345–1363 (2003)CrossRefGoogle Scholar
  6. 6.
    Kumar, K.: Technology for supporting supply-chain management. Comms. of the ACM 44(6), 58–61 (2001)CrossRefGoogle Scholar
  7. 7.
    Luo, X., Jennings, N.R., Shadbolt, N., Leung, H.F., Lee, J.H.M.: A fuzzy constraint based model for bilateral, multi-issue negotiation in semi-competitive environments. Artificial Intelligence 148(1-2), 53–102 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Sugeno, M.: An introductory survey of fuzzy control. Information Sciences 36, 59–83 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Wellman, M.P., Estelle, J., Singh, S., et al.: Strategic interactions in a supply chain game. Computational Intelligence 21(1), 1–26 (2005)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Yen, J.: Fuzzy logic — a modern perspective. IEEE Trans. Knowledge and Data Engineering 11(1), 153–165 (1999)CrossRefGoogle Scholar
  11. 11.
    Zimmermann, H.-J.: Fuzzy Set Theory and Its Applications, ch. 11, pp. 203–240. Kluwer Academic Publishers, Dordrecht (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Minghua He
    • 1
  • Alex Rogers
    • 1
  • Esther David
    • 1
  • Nicholas R. Jennings
    • 1
  1. 1.School of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK

Personalised recommendations