Advertisement

Petri Net Modeling Method to Scheduling Problem of Holonic Manufacturing System (HMS) and Its Solution with a Hybrid PSO Algorithm

  • Fuqing Zhao
  • Qiuyu Zhang
  • Yahong Yang
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 344)

Abstract

Holonic manufacturing is a highly distributed control paradigm based on a kind of autonomous and cooperative entity called “holon”. It can both guarantee performance stability, predictability and global optimization of hierarchical control, and provide flexibility and adaptability of heterarchical control. In this paper, A new class of Time Petri Nets(TPN), Buffer-nets, for defining a Scheduling Holon is proposed, A TPN represents a set of established contracts among the agents in HMS to fulfill an order. To complete processing of orders, liveness of TPNs must be maintained. As different orders may compete for limited resources, conflicts must be resolved by coordination among TPNs. A liveness condition for a set of TPNs is provided to facilitate feasibility test of commitments. which enhances the modeling techniques for manufacturing systems with features that are considered difficult to model. A scheduling architecture, which integrates TPN models and AI techniques is proposed. By introducing dynamic individuals into the reproducing pool randomly according to their fitness, a variable population-size genetic algorithm is presented to enhance the convergence speed of GA. Based on the Novel GA and the particle swarm optimization (PSO) algorithms, a Hybrid PSO-GA algorithm (HPGA) is also proposed in this paper. Simulation results show that the proposed method are effective for the optimization problems.

Keywords

Particle Swarm Optimization Schedule Problem Manufacture Execution System Minimum Time Function Holonic Manufacturing System 
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.
    Balasubramanian, S., Brennan, R. W., Norrie, D. H.: An Architecture for Metamorphic Control of Holonic Manufacturing Systems. Computers in Industry, 46 (2001) 1679–1684CrossRefGoogle Scholar
  2. 2.
    Wyns, J.: Reference Architecture for Holonic Manufacturing. Ph.D. dissertation, PMA Division, Katholieke Universiteit, Leuven, Belgium (1999)Google Scholar
  3. 3.
    Hsieh, Fu-Shiung: Model and Control Holonic Manufacturing Systems Based on Fusion of Contract Nets and Petri Nets. Automatica, 40 (2004) 51–57zbMATHMathSciNetCrossRefGoogle Scholar
  4. 4.
    Babiceanu, R.F., Chen, F.F., Sturges, R.H: Framework for the Control of Automated Material-handling Systems Using the Holonic Manufacturing Approach. International Journal of Production Research, 42 (2004) 3551–3564CrossRefGoogle Scholar
  5. 5.
    Koestler, A.: The Ghost in the Machine. London, Hutchinson (1967)Google Scholar
  6. 6.
    Xu Rui, Cui Pingyuan, Xu Xiaofei: Realization of Multi-agent Planning System for Autonomous Spacecraft. Advances in Engineering Software, 36 (2005) 266–272CrossRefGoogle Scholar
  7. 7.
    Earl, Matthew G., D’Andrea, Raffaello: Modeling and Control of a Multi-agent System Using Mixed Integer Linear Programming. Proceedings of the IEEE Conference on Decision and Control, (2002) 107–111Google Scholar
  8. 8.
    Suesut, T., Tipsuwanporn, V., Nilas, P. et al: Multi Level Contract Net Protocol Based on Holonic Manufacturing System Implement to Industrial Networks. IEEE Conference on Robotics, Automation and Mechatronics, (2004) 253–258Google Scholar
  9. 9.
    Mondal Samrat, Tiwari, M.K: Application of an Autonomous Agent Network to Support the Architecture of a Holonic Manufacturing System. International Journal of Advanced Manufacturing Technology, 20(2002) 931–342CrossRefGoogle Scholar
  10. 10.
    Fletcher, M., Deen, S.M.: Fault-tolerant Holonic Manufacturing Systems. Concurrency Computation Practice and Experience, 13 (2001) 43–70zbMATHCrossRefGoogle Scholar
  11. 11.
    Valckenaers, P., Van Brussel, H.: Holonic Manufacturing Execution Systems. CIRP Annals — Manufacturing Technology, 54 (2005) 427–432Google Scholar
  12. 12.
    Wyns, Jo, Van Brussel, Hendrik, Valckenaers, Paul: Design Pattern for Deadlock Handling in Holonic Manufacturing Systems. Production Planning and Control, 10 (1999) 616–626CrossRefGoogle Scholar
  13. 13.
    Hsieh Fu-Shiung: Deadlock Free Task Distribution and Resource Allocation for Holonic Manufacturing Systems Based on Multi-agent Framework. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, (2001) 2463–2468Google Scholar
  14. 14.
    Oduguwa, V., Tiwari, A., Roy, R.: Evolutionary Computing in Manufacturing Industry: An Overview of Recent Applications. Applied Soft Computing Journal, 5 (2005) 281–299CrossRefGoogle Scholar
  15. 15.
    Nojiri, H: An Evolutionary Computing Approach to Hierarchical Team Decision Problems. International Journal of Smart Engineering System Design, 5 (2003) 47–53CrossRefGoogle Scholar
  16. 16.
    J.H. Holland: Adaptation in Natural and Artificial System. The University of Michigan Press, Ann Arbor, MI (1975)Google Scholar
  17. 17.
    D.E. Goldberg: Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley, Reading, MA (1989)zbMATHGoogle Scholar
  18. 18.
    J. Arabas, Z. Michalewicz, J. Mulawka.: GAVaPS-a genetic Algorithm with Varying Population Size. in: Proc. 1st IEEE Conf. on Evolutionary Computation, Orlando, FL, IEEE Service Center, Piscataway, NJ (1994) 73–78Google Scholar
  19. 19.
    T. Bäck, A.E. Eiben, N.A.L. van der Vaart.: An Empirical Study on GAs “without parameters”. Proc. 6th Conf. on Parallel Problem Solving from Nature, Paris, France, Lecture Notes in Comput. Sci., vol. 1917, Springer, Berlin (2000) 315–324Google Scholar
  20. 20.
    A.E. Eiben, E. Marchiori, V.A. Valko: Evolutionary Algorithms with On-the-fly Population Size Adjustment. Proc. 8th Conf. on Parallel Problem Solving from Nature, Birmingham, UK, Lecture Notes in Computer Science, vol. 3242, Springer, Berlin(2004) 41–50Google Scholar
  21. 21.
    J. Kennedy, R.C. Eberhart: Particle Swarm Optimization. Proc. IEEE Internat. Conf. on Neural Networks, Perth, Australia, vol. IV, IEEE Service Center, Piscataway, NJ (1995) 1942–1948Google Scholar
  22. 22.
    Vanden, B., Frans, E., Andries, P.: A Cooperative Approach to Particle Swam Optimization. IEEE Transactions on Evolutionary Computation (2004) 225–239Google Scholar
  23. 23.
    Coello, C., Carlos, A., Pulido, G. Lechuga, M.: Handling Multiple Objectives with Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation (2004) 256–279Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fuqing Zhao
    • 1
  • Qiuyu Zhang
    • 1
  • Yahong Yang
    • 2
  1. 1.School of Computer and CommunicationLanzhou University of TechnologyLanzhouP.R. China
  2. 2.College of Civil EngineeringLanzhou University of TechchnologyLanzhouP.R. China

Personalised recommendations