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Vol. 16, Special Issue, November 2012, pp. 116-127

 

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Job-shop Scheduling Problem Based on Particle Swarm Optimization Algorithm

 
Ying Sun and Hegen Xiong

College of Machinery and Automation,

Box 242, Wuhan University of Science and Technology, Wuhan, 430081, China

Tel.: +86-027-68862283

E-mail: wustsunying@126.com

 

 

Received: 11 September 2012   /Accepted: 11 October 2012   /Published: 20 November 2012

Digital Sensors and Sensor Sysstems

 

Abstract: Production scheduling is a hotspot of manufacturing system and the core of the whole advanced manufacturing system to achieve the development of management technology, optimize technology, automation and computer technology. The research and application of effective scheduling method and optimization technology is the foundation and the key to realize advanced manufacturing and improve production efficiency. And algorithm research is one of the important content of the production scheduling problem. In recent years, various intelligent computation methods have been gradually introduced into the scheduling problem, such as genetic algorithm and simulated annealing algorithm, etc. In view of the standard particle swarm optimization algorithm can not solve the complexity of the production of job-shop scheduling problem. The metropolis sampling criteria is introduced into the PSO algorithm. Other algorithms combined with particle swarm optimization algorithm, three kinds of fusion simulated annealing thoughts of hybrid particle swarm algorithm are constructed respectively. Comparing the results of hybrid PSO with the other algorithms in scheduling the job-shop benchmarking problem, the effectiveness and superiority of the hybrid Particle Swarm algorithm are verified.

 

Keywords: Job-shop scheduling, Particle swarm algorithm, Simulated annealing algorithm

 

 

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