电子装配物联制造中智能排程算法研究
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摘要
针对目前电子装配车间自动化水平低、传统作业过程中产品加工装配效率低下的问题,提出了一种适用于电子装配车间生产智能排程的改进遗传退火算法。首先,根据电子装配车间特点建立了车间排程的数学模型,该模型以最小化最大完成时间和客户满意度指标为总目标函数。其次,在遗传算法的基础上,引入了模拟退火的思想,以模拟退火替代变异操作保证基因的多样性,引入了最优解存储器,保证适应度值一直往最优化方向发展。最后,采用FT06基准问题数据验证了遗传退火算法的有效性,并给出了动态干扰下的再调度方法及甘特图。
In view of the low level of automation in the electronic assembly workshop and the low efficiency of product processing and assembly in the traditional operation process,it put forward an improved genetic annealing algorithm for production intelligent scheduling in electronic assembly workshop. First,according to the characteristics of the electronic assembly workshop,the mathematical model of the workshop scheduling was established. The model took minimizing the maximum completion time and customer satisfaction as the total objective function. Secondly,on the basis of genetic algorithm,the idea of simulated annealing was introduced. And replace mutation with simulated annealing to ensure the genetic diversity,the optimal solution was introduced to ensure fitness value developing to the optimization direction. Finally,the validity of the genetic annealing algorithm was verified by using the FT06 reference problem data,and the rescheduling method and Gantt chart under dynamic interference were presented.
In view of the low level of automation in the electronic assembly workshop and the low efficiency of product processing and assembly in the traditional operation process,it put forward an improved genetic annealing algorithm for production intelligent scheduling in electronic assembly workshop. First,according to the characteristics of the electronic assembly workshop,the mathematical model of the workshop scheduling was established. The model took minimizing the maximum completion time and customer satisfaction as the total objective function. Secondly,on the basis of genetic algorithm,the idea of simulated annealing was introduced. And replace mutation with simulated annealing to ensure the genetic diversity,the optimal solution was introduced to ensure fitness value developing to the optimization direction. Finally,the validity of the genetic annealing algorithm was verified by using the FT06 reference problem data,and the rescheduling method and Gantt chart under dynamic interference were presented.
引文
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[17]赵诗奎.求解柔性作业车间调度问题的两级邻域搜索混合算法[J].机械工程学报,2015,51(14):175-184.
[1]王雪阳.基于改进启发式搜索算法的农机调度问题研究[J].信息系统工程,2015,(9):109.
[2]Leandro Vitor,Caliane Bastos.Heat Exchanger Network Synthesis without stream splits using parallelized and simplified simulated Annealing and Particle Swarm Optimization[J].Chemical Engineering Science,2017:158.
[4]戴晓辉,李敏强,寇纪淞.遗传算法理论研究综述[J].控制与决策,2000,15(3):263-268.
[5]Fonseca D J,Nacaresse D.Artificial neural networks for job shop simulation[J].Advanced Engineering Informatics,2002,16(4):241-246.
[6]倪庆剑,邢汉承,张志政.蚁群算法及其应用研究进展[J].计算机应用与软件,2008,25(8):12-15.
[7]吴晓军,杨战中,赵明.均匀搜索粒子群算法[J].电子学报,2011,39(6):1261-1266.
[8]张超群,郑建国,王翔.蜂群算法研究综述[J].计算机应用研究,2011,28(9):3201-3205.
[9]杨娇,叶春明.应用新型萤火虫算法求解Job-shop调度问题[J].计算机工程与应用,2013,49(11):213-215.
[10]蔡良伟,李霞.基于混合蛙跳算法的作业车间调度优化[J].深圳大学学报理工版,2010,27(4):391-395.
[11]Reeves C R.A genetic algorithm for flow-shop sequencing[J].Computer&operations research,1995,22(1):5-13.
[12]Murata T,Ishibuchi H,Tanaka H.Genetic algorithms for flow-shop scheduling problem[J].Computers&Industrial Engineering,1996,30(4):1061-1071.
[13]王金鹏,朱洪俊,周俊.最优子种群遗传算法求解柔性流水车间调度问题[J].计算机应用研究,2012,29(2):442-444.
[14]王军强,郭银洲,崔福东等.基于多样性增强的自适应遗传算法的开放式车间调度优化[J].计算机集成制造系统,2014,20(10):2479-2493.
[15]王雷,蔡劲草,石鑫.基于改进遗传算法的多目标柔性作业车间节能调度问题[J].南京理工大学学报(自然科学版),2017,41(4):494-502.
[16]程子安,童鹰,申丽娟等.双种群混合遗传算法求解柔性作业车间调度问题[J].计算机工程与设计,2016,37(06):1636-1642.
[17]赵诗奎.求解柔性作业车间调度问题的两级邻域搜索混合算法[J].机械工程学报,2015,51(14):175-184.
[1]王雪阳.基于改进启发式搜索算法的农机调度问题研究[J].信息系统工程,2015,(9):109.
[2]Leandro Vitor,Caliane Bastos.Heat Exchanger Network Synthesis without stream splits using parallelized and simplified simulated Annealing and Particle Swarm Optimization[J].Chemical Engineering Science,2017:158.