基于改进模拟退火-遗传算法的FMS生产排程优化分析
陈应飞1,彭正超1,胡晓兵1,李彦儒2
(1.四川大学 机械工程学院,四川 成都 610065;2.中国核动力研究设计院,四川 成都 610200)
摘要:制造型企业在采用柔性制造系统进行产品多品种生产加工时,车间生产调度问题(即生产排程)也是影响产品生产效率的一大重要因素。生产排程问题实际上是一个NP-C问题,它没有一个确定的解,只能是结果趋于最优,所以许多科研人员采用遗传算法来解决柔性制造系统的生产排程问题。本文以某机床厂的机床关键箱体类零件为研究对象,在遗传算法的基础上融入粒子群优化算法和模拟退火算法,对遗传算法的初始种群、交叉和变异操作等进行处理,期望能够使算法的收敛速度更快、结果更优。以该机床厂的需求为例,以成组布局的方式进行生产设备的布局,运用Matlab软件对其生产排程方案进行优化分析,结果表示针对该生产对象提出的生产排程优化算法有效可行,可以在实际生产中加以运用。 
关键词:柔性制造系统;生产排程;模拟退火遗传算法;Matlab优化
中图分类号:TH164;TP315 文献标志码:A doi:10.3969/j.issn.1006-0316.2021.02.002
文章编号:1006-0316 (2021) 02-0007-10
Analysis of FMS Scheduling Optimization Based on Improved Simulated Genetic Annealing Algorithm
CHEN Yingfei1,PENG Zhengchao1,HU Xiaobing1,LI Yanru2
( 1. School of Mechanical Engineering, Sichuan University, Chengdu 610065, China; 2.Nuclear Power Institute of China, Chengdu 610200, China)
Abstract:Job scheduling problem (production scheduling) is an important factor affecting the production efficiency when using FMS in production and processing. Production scheduling is actually a problem of NP-C. There is no definite solution for the problem, but the results can be optimized. Many researchers use genetic algorithm to solve FJSP. This paper studies the key box parts of machine tools in a machine tool factory. We incorporate GA with PSO and SA to deal with the initial population, crossover and mutation operations of GA, hoping to make the algorithm converge faster and get better results. The equipment was laid out in groups. The production scheduling scheme was optimized and analyzed by MATLAB. The results show that the production scheduling optimization algorithm proposed for this factory is effective and feasible. It can be applied in actual production.
Key words:flexible manufacturing system;production scheduling;improved simulated annealing genetic algorithm;Matlab optimization
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收稿日期:2020-06-01
基金项目:国家科技重大专项(2018ZX04032001)
作者简介:陈应飞(1994-),男,四川成都人,硕士研究生,主要研究方向为智能制造、数字化生产,E-mail:1564647444@qq.com;胡晓兵(1970-),男,湖北武汉人,博士,教授,主要研究方向为企业信息化、机器视觉、数字化车间,E-mail:huxb@ scu.edu.cn。
 

 

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