[1]范金松 严洪森 周久海 蒋南云.基于遗传算法的某航空发动机装配车间优化调度[J].计算机技术与发展,2012,(09):205-209.
　FAN Jin-song,YAN Hong-sen,ZHOU Jiu-hai,et al.Optimizing Aeroengiae Assembly Shop Schedule Based on Genetic Algorithm[J].,2012,(09):205-209.
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基于遗传算法的某航空发动机装配车间优化调度()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2012年09期
页码:
205-209
栏目:
应用开发研究
出版日期:
1900-01-01

文章信息/Info

Title:
Optimizing Aeroengiae Assembly Shop Schedule Based on Genetic Algorithm
文章编号:
1673-629X（2012）09-0205-05
作者:
范金松1 严洪森2 周久海1 蒋南云1
[1]东南大学复杂工程系统测量与控制教育部重点实验室[2]东南大学硇动化学院
Author(s):
FAN Jin-song;  YAN Hong-sen;  ZHOU Jiu-hai;  JIANG Nan-yun
[1]Ministry of Education Key Laboratory of Measurement and Control of CSE, Southeast University[2]School of Automation, Southeast University
关键词:
遗传算法; 可重入混合车间; 生产调度
Keywords:
genetic algorithm;  re-entrant hybrid flowshop;  production scheduling
分类号:
TP391 TH166
文献标志码:
A
摘要:
航空发动机装配车间装配生产线的调度问题，是一类比较典型的混合Flowshop问题，同时还带有工件可重人等特点，这就区别于一般的Flowshop和Jobshop调度问题，因此，将可重入混合车间调度问题划为第三类调度问题。关于重入式混合车间生产调度的优化问题通常来说都是属于NP难问题。文中通过某航空发动机装配车间生产线的研究，以最小化最大完工时间为目标函数，借助随机矩阵的编码方式和改进的交叉方法与变异方法，提出了基于遗传算法的调度优化方法。最后实验结果表明，文中提出的改进算法能够有效地实现装配车间调度的优化
Abstract:
Assembly line scheduling problem of aeroengine assembly plant is a typical class of hybrid flowshop problem, and also features a piece re-entrant, which is different from the general flowshop and jobshop scheduling. Therefore, designate re-entrant hybrid flowshop as a third of scheduling problems. Re-entry hybrid plant production scheduling optimization problems are usually NP-hard ones. In this paper,through a study on an aero engine assembly plant production line,minimizing the makespan as the objective function and by means of random matrix encoding and improved crossover and mutation methods,an approach to optimize schedules is proposed based on genetic algorithm. Finally, simulation experiments show that the proposed improved algorithm can effectively achieve the assembly shop scheduling optimization

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备注/Memo

备注/Memo:
国家自然科学基金资助项目（60934008）范金松（1987-），男,江苏沭阳人，硕士研究生，研究方向为生产计划与调度；严洪森，博士，教授，博士生导师，研究方向为知识化制造、生产计划与调度、预测等
更新日期/Last Update: 1900-01-01