The Improvement and Optimization of Job Shop Scheduling Problem Based on Genetic Algorithm

摘要：

This paper analyzes the mathematical model of Job Shop Scheduling Problem and improves traditional Genetic Algorithms by simplifying coding,optimizing crossover and mutation operator,and introduces selection operator with sifting strategy.The simulation results show that the global search ability is greatly better than that of traditional method.The improved Genetic Algorithms can solve Job Shop Scheduling Problem effectively.

关键词： genetic algorithm job shop scheduling problem sifting strategy

作者: Wang Zhengcheng Zhou Shuang

作者单位: Economic and Management Department ZheJiang Sci-Tech University HangZhou, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 135-138

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）