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计算机工程 ›› 2010, Vol. 36 ›› Issue (14): 201-203. doi: 10.3969/j.issn.1000-3428.2010.14.073

• 工程应用技术与实现 • 上一篇    下一篇

基于自适应遗传算法的流水车间作业调度

沈 斌1,周莹君2,3,王家海2   

  1. (1. 同济大学中德学院,上海 200092;2. 同济大学机械工程学院,上海 200092;3. 同济大学经济与管理学院,上海 200092)
  • 出版日期:2010-07-20 发布日期:2010-07-20
  • 作者简介:沈 斌(1955-),男,教授、博士、博士生导师,主研 方向:现代集成制造,网络化设计与制造,数字化制造;周莹君,博士、博士后;王家海,副教授、博士

Flow Job Shop Scheduling Based on Self-adaptive Genetic Algorithm

SHEN Bin1, ZHOU Ying-jun2,3, WANG Jia-hai2   

  1. (1. School of Sino-German, Tongji University, Shanghai 200092; 2. School of Mechanical Engineering, Tongji University, Shanghai 200092; 3. School of Economics and Management, Tongji University, Shanghai 200092)
  • Online:2010-07-20 Published:2010-07-20

摘要: 流水车间调度问题是NP完全问题。提出一种新的自适应遗传算法,采用初始种群复合化、适应度相同个体的筛选策略、改进自适应交叉变异概率等方法提高算法性能。通过仿真比较,从最优解出现的代数、最优解的相对误差以及随机若干次试验对算法的影响3个方面证明该算法的优越性。

关键词: 自适应遗传算法, 流水车间作业调度, 算法改进

Abstract: Flow shop scheduling problem is a Non-Polynomial complete problem. This paper presents a new self-adaptive genetic algorithm. Improved methods including compounded initial population, filter stratagem for the individual with same fitness value and improved self-adaptive across aberrance probability are adopted to improve the algorithm performance. The simulation results show that the improved algorithm has high performance in terms of the best result, the relative error of the best results and robustness.

Key words: self-adaptive genetic algorithm, flow job shop scheduling, algorithm improvement

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