A New Genetic Algorithms combined with Learning Strategy for Flexible Job-shop Scheduling Problem

摘要：

In this paper, we have proposed a new method based on genetic algorithms and the learning by partial injection of sequences for solving the Flexible Jobshop Scheduling Problem (FJSP). Computational experiments show that the AGAIS (II) algorithm outperforms the performance of the AGAIS (I). In fact, the AGAIS (II) gives better solutions than AGAIS (I) in a reasonable computation time.

关键词： job-shop scheduling genetic algorithms learning strategy

作者: XIE Shi-man

作者单位: College of Mechanical Engineering Hebei Polytechnic University Tangshan, China

会议类型: 国际会议

会议名称: 2010 Third International Symposium on Intelligent Ubiquitous and Education(2010年第三届智能普适计算与教育国际研讨会 IUCE 2010)

会议地点: 北京

会议语种:英文

页码: 450-453

在线出版日期: 2010-09-18（万方平台首次上网日期，不代表论文的发表时间）