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

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 fulfill 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.