Cooperative Scheduling System with Emergent Swarm Based Behavior

  • Ana Madureira
  • Ivo Pereira
  • Diamantino Falcão
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 206)


This paper presents a Swarm based Cooperation Mechanism for scheduling optimization. We intend to conceptualize real manufacturing systems as interacting autonomous entities in order to support decision making in agile manufacturing environments. Agents coordinate their actions automatically without human supervision considering a common objective – global scheduling solution taking advantages from collective behavior of species through implicit and explicit cooperation. The performance of the cooperation mechanism will be evaluated consider implicit cooperation at first stage through ACS, PSO and ABC algorithms and explicit through cooperation mechanism application.


Cooperation Swarm Intelligence Scheduling Systems Multi-Agent Systems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Diekmann, A., Lindenberg, S.: Sociological aspects of cooperation. In: International Encyclopaedia of the Social Sciences and Behavioral Sciences. Elsevier (2001)Google Scholar
  2. 2.
    Luck, M., McBurney, P., Shehory, O., Willmott, S.: Agent Technology: Computing as Interaction, A Roadmap for Agent-Based Computing, AgentLink III (2005)Google Scholar
  3. 3.
    Dorigo, M.: Swarm Intelligence. Springer, New York (2007)Google Scholar
  4. 4.
    Madureira, A., Pereira, I.: Intelligent Bio-Inspired System for Manufacturing Scheduling under Uncertainties. Int. Jour. of Comp. Inform. Sys. and Ind. Manag. Appli. 3 (2011)Google Scholar
  5. 5.
    Dorigo, M.: Optimization, Learning and Natural Algorithms. PhD Thesis, Politecnico di Milano, Italy (1992) (in Italian)Google Scholar
  6. 6.
    Dorigo, M., Gambardella, L.M.: Ant Colony System: A cooperative learning approach to the traveling salesman problem. IEEE Trans. on Evol. Comp., 53–66 (1997)Google Scholar
  7. 7.
    Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference Neural Networks, pp. 1942–1948 (1995)Google Scholar
  8. 8.
    Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: The Bees Algorithm, Manufacturing Engineering Centre. Cardiff University, United Kingdom (2005)Google Scholar
  9. 9.
    Karaboga, D., Akay, B.: A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems. Applied Soft Computing 11, 3021–3031 (2011)CrossRefGoogle Scholar
  10. 10.
    Nwana, H.S.: Software Agents: An Overview. Knowledge Engineering Review 11(3), 205–244 (1996)CrossRefGoogle Scholar
  11. 11.
    Wooldridge, M., Jennings, N.R.: Intelligent Agents: Theory and Practice. Knowledge Engineering Review 10(2) (1995)Google Scholar
  12. 12.
    Allen-Williams, M.: Coordination in multi-agent systems. Ph.D. Thesis (2005)Google Scholar
  13. 13.
    Huang, Z., Gao, P., He, Y., He, Q.: Multi-Agent Cooperation Based on Interest Group. In: International Conference on Computational Intelligence and Software Engineering (2009)Google Scholar
  14. 14.
    Ouelhadj, D., Petrovic, S., Cowling, P.I., Meisels, A.: Inter-agent cooperation and communication for agent-based robust dynamic scheduling in steel production. Adv. Eng. Inform. 18 (2004)Google Scholar
  15. 15.
    Madureira, A., Ramos, C., Silva, S.C.: A Coordination Mechanism for Real World Scheduling Problems Using Genetic Algorithms. In: IEEE World Congress on Computational Intelligence (CEC 2002), Honolulu - Hawai (EUA) (2002)Google Scholar
  16. 16.
  17. 17.
    Villegas, J.G.: Using nonparametric test to compare the performance of Metaheuristics (2012),

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ana Madureira
    • 1
  • Ivo Pereira
    • 1
  • Diamantino Falcão
    • 1
  1. 1.GECAD - Knowledge Engineering and Decision Support Research CenterInstitute of Engineering – Polytechnic of Porto (ISEP/IPP)PortoPortugal

Personalised recommendations