An Algorithm for solving Flexible Job shop Scheduling Problems Based on Multi-Objective Particle Swarm Optimization

摘要：

A new method based on multi-objective particle swarm optimization is proposed to deal with the flexible job shop scheduling problems with multiple objectives, minimizing completion time, total machine workload and the biggest machine workload. This algorithm adopts linear weighting method to change multi-objective optimization problem into the single objective optimization problem, and introduces random and uniform design method to produce weight coefficient, which ensures the diversity and uniform distribution of pareto set Besides, elite reserved strategy and dynamic neighborhood operator are designed to maintain the diversity of population and improve search capabilities of particles. Particle is presented in the form of binary group. In order to solve process scheduling priority issues and machinery distribution, encoding process, consisting of extended operation and priority rule, is designed. Finally, the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for the flexible job shop scheduling problems.

关键词： Flexible Job Shop Scheduling Problem Uniform design method Dynamic neighborhood operator Multi-Objective Particle Swarm Optimization

作者: HU Nai-ping Wang Pei-li

作者单位: College of Information Science and Technology Qingdao University of Science and Technology Qingdao, China

会议类型: 国际会议

会议名称: Third International Symposium on Information Science and Engineering(第三届信息科学与工程国际会议 ISISE 2010)

会议地点: 上海

会议语种:英文

页码: 507-511

在线出版日期: 2010-12-24（万方平台首次上网日期，不代表论文的发表时间）