基于NSGAⅡ和神经网络的织造车间大规模调度  

Large-scale scheduling of weaving workshop based on NSGAII and neural network

作　　者：雷钧杰 沈春娅 胡旭东[1,2] 汝欣 彭来湖[1,2] LEI Junjie;SHEN Chunya;HU Xudong;RU Xin;PENG Laihu(Faculty of Mechanical Engineering&Automation,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China;Key Laboratory of Modern Textile Machinery&Technology of Zhejiang Province,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China)

机构地区：[1]浙江理工大学机械与自动控制学院,浙江杭州310018 [2]浙江理工大学浙江省现代纺织装备技术重点实验室,浙江杭州310018

出　　处：《纺织学报》2023年第11期208-215,共8页Journal of Textile Research

基　　金：浙江省公益技术研究计划项目(LGG21E050024);浙江省重点研发计划项目(2019C01038);浙江省博士后科研项目特别资助项目(ZJ2020004);浙江理工大学科研启动基金项目(18022224-Y)。

摘　　要：为解决遗传算法在织造车间大规模调度中容易陷入局部最优的问题,提出了NSGAⅡ-NN125调度算法。首先,根据织造车间大规模调度的特点,以最小化逾期损失、完工时间和改车次数为优化目标,建立了织造车间调度模型。然后设计了以神经网络模型NN125为主体的调度模块,其可根据织轴和织机特征信息生成调度方案。最后,设计了以NSGAⅡ为主体的优化模块,其根据方案优劣对调度模块中的NN125进行优化。结果表明:NSGAⅡ-NN125的调度质量随着调度规模的不断增大始终非常稳定,而且已优化的调度模块可直接用于相似问题的调度,调度性能较好,由于省去了优化过程,调度速度(约50个织轴/s)也有较大提升,具有较好的实用价值。Objective With the increase of personnel,machines and materials,the scheduling scale of weaving workshop increases exponentially.The intelligent scheduling algorithm represented by genetic algorithm is easy to fall into the local optimal solution when solving large-scale scheduling problems,and the process is slow,which is difficult to meet the actual demand.This study aims to combine the advantages of genetic algorithm and neural network to solve the problem of large-scale scheduling in weaving workshop.Method According to the characteristics of large-scale scheduling of weaving workshop,a weaving workshop scheduling model was established to minimize overdue loss,the makespan and the number of variety changes.Then,a weaving workshop scheduling algorithm NSGAII-NN125 based on NSGAII and neural network was proposed to solve the large-scale scheduling problem of weaving workshop,which consists of a scheduling module and a multi-objective optimization module.Finally,the optimization module was adopted to find the best the scheduling module according to the merits and demerits of the generation scheme,leading to the scheduling module with high quality,fast speed and reusable.Results Comparing the objectives of minimizing overdue loss,the makespan and the number of variety changes,NSGAII-NN125 offered stable performance in a series of weaving workshop scheduling,especially in large-scale scheduling with more than 300 looms and more than 2000 weaver′s beams(Tab.3).The optimization does not fall into the trend of local optimal solution,and the solution quality is outstanding.Compared with the optimization time,NSGAII-NN125 needed to take longer time to calculate and update the eigenvalues of the neural network.The scheduling speed of NSGAII-NN125 was about 0.67 weaver′s beams per second.The NN125 model set was optimized by NSGAII-NN125 according to the scheduling requirements of a weaving workshop which can be used for scheduling similar requirements.Compared with the scheduling objectives,it can be seen that the sc

关 键 词：织造车间 大规模调度 NSGAⅡ 神经网络 多目标调度 智能调度 

分 类 号：TS111.8[轻工技术与工程—纺织材料与纺织品设计]