A Scheduling Holon Modeling Method with Petri Net and its Optimization with a Novel PSO-GA Algorithm

Holonic manufacturing systems (HMS) provide a flexible and decentralized manufacturing environment to accommodate changes dynamically. This paper presents a framework to model and control HMS based on Petri net and MAS theory. A time Petri net (TPN) model was proposed to achieve this goal. A TPN represents a set of established contracts among the agents in HMS to fulfil an order. 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 is effective for the optimization problems

[1]  Robert W. Brennan,et al.  An architecture for metamorphic control of holonic manufacturing systems , 2001, Comput. Ind..

[2]  Zeng Xiao-qing,et al.  Multi-agent Learning Negotiation Research in Virtual Enterprise Based on Contract Net , 2004 .

[3]  Thomas Bäck,et al.  An Empirical Study on GAs "Without Parameters" , 2000, PPSN.

[4]  Luc Bongaerts,et al.  Holonic manufacturing systems , 1997 .

[5]  Elena Marchiori,et al.  Evolutionary Algorithms with On-the-Fly Population Size Adjustment , 2004, PPSN.

[6]  Hendrik Van Brussel,et al.  Design pattern for deadlock handling in holonic manufacturing systems , 1998 .

[7]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[8]  Farzad Mahmoodi,et al.  A comparison of deadlock avoidance policies in flexible manufacturing systems , 2003 .

[9]  Zhou Wei,et al.  A MES design based multi-Agent system , 2002, Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527).

[10]  Y.-E. Nahm,et al.  A hybrid multi-agent system architecture for enterprise integration using computer networks , 2005 .

[11]  R. H. Sturges,et al.  Framework for the control of automated material-handling systems using the holonic manufacturing approach , 2004 .

[12]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[13]  Yasumichi Aiyama,et al.  Holonic Manufacturing System , 2000 .

[14]  Cheng Wu,et al.  Scheduling algorithm based on evolutionary computing in identical parallel machine production line , 2003 .

[15]  Zbigniew Michalewicz,et al.  GAVaPS-a genetic algorithm with varying population size , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.