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Content Provider | IEEE Xplore Digital Library |
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Author | Fuqing Zhao Qiuyu Zhang Yahong Yang |
Copyright Year | 2006 |
Description | Author affiliation: Sch. of Comput. & Commun. Eng., Lanzhou Univ. of Technol., Gansu (Fuqing Zhao; Qiuyu Zhang) |
Abstract | 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 |
Sponsorship | IEEE Beijing Section |
Starting Page | 1 |
Ending Page | 6 |
File Size | 6563166 |
Page Count | 6 |
File Format | |
ISBN | 142440164X |
DOI | 10.1109/CSCWD.2006.253036 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2006-05-03 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Optimization methods Scheduling algorithm Job shop scheduling Manufacturing systems Flexible manufacturing systems Contracts Artificial intelligence Genetic algorithms Convergence Particle swarm optimization genetic algorithms Holonic manufacturing system time Petri net particle swarm optimization |
Content Type | Text |
Resource Type | Article |
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