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一种基于遗传算法的作业车间调度问题的解决方案

发表时间:2018-11-30  浏览量:41  下载量:7
全部作者: 陈浩哲,王晨升,朱宏波,李佳,杨光,贾智涵
作者单位: 北京邮电大学自动化学院
摘 要: 作业车间调度问题(job-shop scheduling problem,JSP)是复杂调度问题类型之一,有着十分重要的研究意义和工程价值。本文以标准遗传算法为基础,在充分利用遗传信息的改进思路下,将标准遗传算法与神经网络进行融合,以此提出改进算法。改进算法以标准遗传算法为框架,将神经网络嵌套进遗传算法计算子代适应度,并根据适应度对交叉点进行选择。最后以MT06为例,将改进算法与标准遗传算法进行对比。实验结果表明,改进算法相对于标准遗传算法在求解质量与收敛效率方面均存在一定的提升。
关 键 词: 自动控制技术;作业车间调度问题;遗传算法;神经网络
Title: A solution to job-shop scheduling problem based on genetic algorithm
Author: CHEN Haozhe, WANG Chensheng, ZHU Hongbo, LI Jia, YANG Guang, JIA Zhihan
Organization: School of Automation, Beijing University of Posts and Telecommunications
Abstract: Job-shop scheduling problem (JSP) is one of the types of complex scheduling problems, which has very important research significance and engineering value. In this paper, based on the standard genetic algorithm, in the full use of genetic information to improve the idea, the standard genetic algorithm is fused with neural network, so as to propose an improved algorithm. The improved algorithm uses the standard genetic algorithm as the framework, nests the neural network into the genetic algorithm to calculate the progeny fitness, and selects the intersection point according to the fitness. Finally, taking MT06 as an example, the improved algorithm is compared with the standard genetic algorithm. The experimental results show that the improved algorithm has some improvement in the solution quality and convergence efficiency compared with the standard genetic algorithm.
Key words: autocontrol technology; job-shop scheduling problem; genetic algorithm; neural network
发表期数: 2018年11月第22期
引用格式: 陈浩哲,王晨升,朱宏波,等. 一种基于遗传算法的作业车间调度问题的解决方案[J]. 中国科技论文在线精品论文,2018,11(22):2287-2294.
 
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