Advertisement

Multiagent Based Simulation as a Supply Chain Analysis Workbench

  • Jacek Jakieła
  • Paweł Litwin
  • Marcin Olech
Chapter
  • 392 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7190)

Abstract

The paper presents the application of Multiagent Based Simulation to analysis of supply chain behavior. As has been shown in the paper, agent oriented approach may be considered as a powerful conceptual framework for organization modeling and workbench for simulations of intra- and inter-organizational business processes. All of these theses have gradually been proved in the subsequent sections of the article. Firstly the agent paradigm has been presented as a toolbox for business modeling and complexity management. Then the classical model of supply chain simulation has been transformed to its agent-based version. Finally the case study presents how the agent model of the supply chain may be used in the process of bullwhip effect analysis based on the simulation experiment.

Keywords

agent-oriented modeling agent-oriented simulation business modeling supply chain modeling extended enterprise simulation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    North, M.J., Macal, C.M.: Managing Business Complexity. Discovering Strategic Solutions with Agent-Based Modeling and Simulation. Oxford University Press (2007)Google Scholar
  2. 2.
    Tapscott, D., Ticoll, D., Lowy, A.: Digital Capital: Harnessing the Power of Business Webs. Harvard Business Press (2000)Google Scholar
  3. 3.
    Vieira, G.E., Cesar Jr., O.: A conceptual model for the creation of supply chains models. In: Proceedings of the 37th conference on Winter simulation, Orlando, Florida, pp. 2619–2627 (2005)Google Scholar
  4. 4.
    Jakieła, J., Litwin, P., Olech, M.: MAS Approach to Business Models Simulations: Supply Chain Management Case Study. In: Jędrzejowicz, P., Nguyen, N.T., Howlet, R.J., Jain, L.C. (eds.) KES-AMSTA 2010, Part II. LNCS (LNAI), vol. 6071, pp. 32–41. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Jakieła, J., Litwin, P., Olech, M.: Toward the Reference Model for Agent-based Simulation of Extended Enterprises. In: Setlak, G., Markov, K. (eds.) Methods and Instruments of Artificial Intelligence, pp. 34–66 (2010)Google Scholar
  6. 6.
    Weyns, D., Uhrmacher, A.M. (eds.): Multi-Agent Systems Simulation and Applications. Computational Analysis, Synthesis, and Design of Dynamic Models Series. CRC Press, Florida (2009)Google Scholar
  7. 7.
    Luck, M., McBurney, P., Preist, C.: Agent technology: enabling next generation computing. A Roadmap for Agent Based Computing (2003), www.agentlink.org
  8. 8.
    Davidsson, P.: Agent based social simulation: a computer science view. J. Artif. Soc. Social Simulation 5 (2002)Google Scholar
  9. 9.
    Morgan, G.: Images of organizations. Sage Publications (2006)Google Scholar
  10. 10.
    Jakieła, J.: AROMA – Agentowo zoRientowana metOdologia Modelowania orgAnizacji. WAEiI, Politechnika Slaska, Gliwice (2006)Google Scholar
  11. 11.
    Peppard, J., Rowland, P.: Reengineering, Gebethner i S-ka. Warszawa (1997) Google Scholar
  12. 12.
    Drucker P. F.: Zarządzanie w XXI wieku. Muza S.A (2000) Google Scholar
  13. 13.
    Hammer M., Champy J.: Reengineering w przedsiębiorstwie. Neumann Management Institute, Warszawa (1996) Google Scholar
  14. 14.
    Hammer. M.: Reinzynieria i jej następstwa. PWN, Warszawa (1999) Google Scholar
  15. 15.
    Simon, H.: The Sciences of Artificial. MIT Press (1996) Google Scholar
  16. 16.
    Jennings, N.R., Wooldridge, M.: Agent-Oriented Software Engineering. In: Garijo, F.J., Boman, M. (eds.) MAAMAW 1999. LNCS, vol. 1647, pp. 1–7. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  17. 17.
    Cetnarowicz, K.: Problemy projektowania i realizacji systemów wielo-agentowych. Uczelniane wydawnictwa naukowo-dydaktyczne, AGH, Krakow (1999) Google Scholar
  18. 18.
    Muckstadt, J., Murray, D., Rappold, J., Collins, D.: Guidelines for collaborative supply chain system design and operation. Information Systems Frontiers 3, 427–435 (2001)CrossRefGoogle Scholar
  19. 19.
    Gilbert, N.: Agent-Based Models. Sage Publications (2007)Google Scholar
  20. 20.
    Paolucci, M., Sacile, R.: Agent-Based Manufacturing and Control Systems. New Agile Manufacturing Solutions for Achieving Peak Performance. CRC Press (2005)Google Scholar
  21. 21.
    Byrne, P.J., Heavey, C.: Simulation, a Framework for analyzing SME supply chains. In: Proceedings of the 2004 Winter Simulation Conference (Winter 2005)Google Scholar
  22. 22.
    Van Dyke Parunak, H.: Applications of distributed artificial intelligence in industry. In: O’Hare, G.M.P., Jennings, N. (eds.) Foundations of Distributed Artificial Intelligence, pp. 71–76. John Wiley and Sons (1996)Google Scholar
  23. 23.
    Kimbrough, S.O., Wu, D., Zhong, F.: Computers play the Beer Game: Can artificial agents manage supply chains? Decision Support Systems 33, 323–333 (2002)CrossRefGoogle Scholar
  24. 24.
    Sterman, J.D.: Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment. Management Science 35, 321–339 (1989)CrossRefGoogle Scholar
  25. 25.
    Chen, F.: Decentralized supply chains subject to information delays. Management Science 45, 1076–1090 (1999)zbMATHCrossRefGoogle Scholar
  26. 26.
    Moyaux, T., Chaib-draa, B., D’Amours, S.: An Agent Simulation Model for the Quebec Forest Supply Chain. In: Klusch, M., Ossowski, S., Kashyap, V., Unland, R. (eds.) CIA 2004. LNCS (LNAI), vol. 3191, pp. 226–241. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  27. 27.
    Nfaoui, E.H., Ouzrout, Y., El Beqqali, O.: An approach of agent-based distributed simulation for supply chains: Negotiation protocols between collaborative agents. In: Proceedings of the 20th annual European Simulation and Modeling Conference, EUROSIS, Toulouse, France, pp. 290–295 (2006)Google Scholar
  28. 28.
    Maturana, F., Shen, W., Norrie, D.: Metamorph: an adaptive agent-based architecture for intelligent manufacturing. International Journal of Production Research 37, 2159–2173 (1999)zbMATHCrossRefGoogle Scholar
  29. 29.
    Sadeh, N.M., Hildum, D.W., Kjenstad, D.: MASCOT: an agent-based architecture for dynamic supply chain creation and coordination in the Internet economy. Prod. Plan. Control 12(3), 212–223 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jacek Jakieła
    • 1
  • Paweł Litwin
    • 1
  • Marcin Olech
    • 1
  1. 1.Faculty of Mechanical Engineering and Aeronautics, Department of Computer ScienceRzeszow University of TechnologyRzeszowPoland

Personalised recommendations