航空学报 > 2001, Vol. 22 Issue (2): 180-183

求解含调整时间排序问题的混合遗传算法

周泓1, 张惠民2   

  1. 1. 北京航空航天大学管理学院,北京100083;2. 香港中文大学工商管理学院,香港,沙田
  • 收稿日期:1999-11-19 修回日期:2000-06-15 出版日期:2001-04-25 发布日期:2001-04-25

HYBRID GENETIC ALGORITHM FOR JOB SHOP SCHEDULING WITH SEQUENCE-DEPENDENT SETUP TIMES

ZHOU Hong1, CHEUNG Waiman2   

  1. 1. School of Management, Beijing University of Aeronautics and Astronautics, Beijing 100083, China;2. Faculty of Business Administration, The Chinese University of Hong Kong, Hong Kong, China
  • Received:1999-11-19 Revised:2000-06-15 Online:2001-04-25 Published:2001-04-25

摘要:

利用仿真工具将启发式方法与遗传算法相结合,提出了一种求解Job Shop排序问题的混合算法框架,利用启发式规则引导遗传搜索过程,以提高遗传算法的求解效率。在求解过程中,遗传算法仅对每台机器的第1道工序搜索寻优,通过仿真过程安排后续工序,在仿真过程中,利用启发式规则确定工件的加工优先级。在以上框架基础上,针对含调整时间的作业排序问题建立了一种混合算法GA-SPTS,通过与已有算法的比较表明,该算法对这类问题具有很好的求解性能。

关键词: 作业排序, 遗传算法, 启发式, 系统仿真, 组合优化

Abstract:

A hybrid algorithm framework is proposed for job shop scheduling, with which heuristic rules are integrated with genetic algorithm by means of simulation. With the framework, the searching efficiency of genetic algorithm can be effectively improved due to the guidance of the heuristic rules properly designed for the specific problems. The hybrid algorithm is implemented by optimizing the first operation of each machine with GA, and arranging the succedent operations through a simulation process in which heuristic rules are employed to determine the processing priority of jobs. Based on the framework, an algorithm, GA\|SPTS, is developed for job shop scheduling problems with sequence dependent setup times, which shows high performance compared with the existing algorithms.

Key words: scheduling, genetic algorithm, heuristic, simulation, combinatorial optimization

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