Chaotic Quantum-behaved Particle Swarm Optimization Based Flow-shop Scheduling

Production scheduling is an very important and comlicated problem every factory or company faces with. It is the foundation and crux to apply efficient production scheduling method and optimization technique when improving production efficiency and comprehensive competitiveness in modern enterprises. Flow shop scheduling problem (FSP) is the main type of production scheduling problems and attracts widespread academic attentions all over the world. On the basis of reviewing production scheduling algorithms, this paper improves the quantum-behaved particle swarm optimization, applying to flow shop scheduling problems and the results turns out to be better.This paper firstly summarizes the background, the category and research methods of production scheduling problems. It then introduces the fundamentals of the particle swarm optimization algorithm (PSO) and the quantum-behaved particle swarm optimization algorithm (QPSO) and also analyzes the advantages and disadvantages of them. In order to further improve the performance of PSO and QPSO, this paper proposes the chaotic quantum-behaved particle swarm optimization (CQPSO) by introducing the chaotic optimization (CO) into the process of QPSO. This method enhances the ability of global search in fact and gains better results theoretically. Afterwards, CQPSO is applied to the permutation flow shop scheduling problem (PFSP) and no-wait flow shop scheduling problem (NWFSP). According to the characteristic of FSP and chaotic system theory, a creative encoding and decoding scheme is proposed and experimented in the simulation program. The results demonstrates that the CQPSO proposed in this paper and applied to PFSP and NWFSP is better than PSO and QPSO.