Advertisement

Agent-Based Adaptive Production Scheduling — A Study on Cooperative-Competition in Federated Agent Architecture

  • Jayeola Femi Opadiji
  • Toshiya Kaihara
Part of the Springer Series on Agent Based Social Systems book series (ABSS, volume 6)

Abstract

An increasingly popular method of improving the performance of complex systems operating in dynamic environments involves modeling such systems as social networks made up of a community of agents working together based on some basic principles of social interaction. However, this paradigm is not without its challenges brought about by the need for autonomy of agents in the system. While some problems can be solved by making the interaction protocol either strictly competitive or strictly cooperative, some other models require the system to incorporate both interaction schemes for improved performance. In this paper, we study how the seemingly contradictory effects of these two behaviours can be exploited for distributed problem solving by considering a flexible job shop scheduling problem in a dynamic order environment. The system is modeled using federated agent architecture. We implement a simple auction mechanism at each processing center and a global reinforcement learning mechanism to minimize cost contents in the system. Results of simulations using the cooperative-competition approach and the strictly competitive model are presented. Simulation results show that there were improvements in cost objectives of the system when the various processing centers cooperated through the learning mechanism, which also provides for adaptation of the system to a stream of random orders.

Keywords

Schedule Problem Multiagent System Inventory Cost Task Sequence Competitive 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Allahverdi, A. and Al-Anzi, F. S.: A branch-and-bound algorithm for three-machine flowshop scheduling problem to minimize total completion time with separate setup times, European Journal of Operational Research, Volume 169, Issue 3, 16 March 2006, Pages 767–780CrossRefGoogle Scholar
  2. [2]
    Aydin, M.E. and Öztemel, E.: Dynamic job-shop scheduling using reinforcement learning agents, Robotics and Autonomous Systems, Volume 33, Issues 2–3, 30 November 2000, Pages 169–178Google Scholar
  3. [3]
    Baguchi, T.P.: Multiobjective Scheduling by Genetic Algorithms, Kluwer Academic Publishers, USA, 1999Google Scholar
  4. [4]
    Brandimarte, P.: Routing and Scheduling in a flexible job shop by tabu search, Annals of Operations Research 41, 1993, Pages 157–183CrossRefGoogle Scholar
  5. [5]
    Loukil, T., Teghem, J. and Fortemps, P.: A multi-objective production scheduling case study solved by simulated annealing, European Journal of Operational Research, Volume 179, Issue 3, 16 June 2007, Pages 709–722CrossRefGoogle Scholar
  6. [6]
    Markus, A, Vancza, T.K., Monostori L.: A Market Approach to Holonic Manufacturing. Annals of CIRP 45 1996. 433–436.CrossRefGoogle Scholar
  7. [7]
    Moursli, O. and Pochet, Y.: A branch-and-bound algorithm for the hybrid flowshop, International Journal of Production Economics, Volume 64, Issues 1–3, 1 March 2000, Pages 113–125CrossRefGoogle Scholar
  8. [8]
    Pinedo, M.: Scheduling, Theory Algorithms and Systems (2nd Edition). Prentice Hall, USA. 2002Google Scholar
  9. [9]
    Sandholm, T.: Distributed Rational Decision Making. Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence (Ed. By Weiss, G.). The MIT Press, USA. 1999.201–258.Google Scholar
  10. [10]
    Sandholm, T.: An Algorithm for Optimal Winner Determination. in Combinatorial Auctions. IJCAI 1999, 542–547.Google Scholar
  11. [11]
    Shen, W., Nome, D.H., Barthes, J.A.: Multi-agent system for concurrent intelligent design and manufacturing. Taylor & Francis Inc. USA. 2001Google Scholar
  12. [12]
    Vancza, J., Markus, A.: An Agent Model for Incentive-Based Production Scheduling. Computers in Industry, Elsevier, 43 2000. 173–187.CrossRefGoogle Scholar
  13. [13]
    Walsh, W.E., Wellman, M.P.: A Market Protocol for Decentralized Task Allocation. Proceedings of the Third International Conference on Multi-Agent Systems, IEEE Computer Society 1998. 325–332Google Scholar
  14. [14]
    Wang, Y and Usher, J.M.: Application of reinforcement learning for agent-based production scheduling, Engineering Applications of Artificial Intelligence, Volume 18, Issue 1, February 2005, Pages 73–82CrossRefGoogle Scholar
  15. [15]
    Weiss, G. (Ed.): A Modern Approach to Distributed Artificial Intelligence, The MIT Press, USA. 1999Google Scholar
  16. [16]
    Zotteri, G. and Verganti, R.: Multi-level approaches to demand management in complex environments: an analytical model, International Journal of Production Economics, Volume 71, Issues 1–3, 6 May 2001, Pages 221–233CrossRefGoogle Scholar

Copyright information

© Springer 2009

Authors and Affiliations

  • Jayeola Femi Opadiji
    • 1
  • Toshiya Kaihara
    • 2
  1. 1.Graduate School of Science and TechnologyKobe UniversityJapan
  2. 2.Graduate School of EngineeringKobe UniversityJapan

Personalised recommendations