Permutation Flow Shop Scheduling Algorithm based on a Hybrid Particle Swarm Optimization

摘要：

The permutation flow shop scheduling problem is a part of production scheduling, which belongs to the hardest combinatorial optimization problem. A new hybrid algorithm is introduced which we called it HPSO, It combines knowledge evolution algorithm(KEA) and particle swarm optimization(PSO) algorithm for the permutation flow shop scheduling problem. The objective function is to search for a sequence of jobs in order that we can obtain the minimization value of maximum completion time (makespan). By the mechanism of KEA, its global search ability is fully utilized for finding the global solution. By the operating characteristic of PSO, the local search ability is also made full use. The experimental results indicate that the solution quality of the permutation flow shop scheduling problem based on HPSO is better than those based on Genetic algorithm, and than those based on standard PSO.

关键词： Flow shop schdeuling Knowledge evolution algoithm Particle swarm optimization

作者: Hai-bo Tang Chun-ming YE

作者单位: College of Management,University of Shanghai for Science and Technology,200093,ShangHai,China

会议类型: 国际会议

会议名称: 2010 IEEE 17th International Conference on Industrial Engineering and Engineering Management(2010年IEEE第17届工业工程与工程管理国际学术会议)

会议地点: 厦门

会议语种:英文

页码: 557-560

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）