您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(工学版)》

山东大学学报 (工学版) ›› 2021, Vol. 51 ›› Issue (4): 17-23.doi: 10.6040/j.issn.1672-3961.0.2021.091

• • 上一篇    

基于狮群算法的数字孪生车间调度问题优化

黄澄1,2,袁东风1,2*,张海霞2,3   

  1. 1.山东大学信息科学与工程学院, 山东 青岛 266237;2.山东省无线通信技术重点实验室, 山东 济南 250100;3.山东大学控制科学与工程学院, 山东 济南 250061
  • 发布日期:2021-08-18
  • 作者简介:黄澄(1997— ),女,江苏镇江人,硕士研究生,主要研究方向为智能算法,人工智能. E-mail:hc970608@163.com. *通信作者简介:袁东风(1958— ),男,四川安岳人,教授,博士,主要研究方向为智能通信系统,移动边缘计算和云计算,人工智能和通信大数据处理. E-mail:dfyuan@sdu.edu.cn
  • 基金资助:
    山东省重点研发计划重大科技创新工程课题资助项目(2019TSLH0202,2019JZZY01011)

Optimization of digital twin job scheduling problem based on lion swarm algorithm

HUANG Cheng1,2, YUAN Dongfeng1,2*, ZHANG Haixia2,3   

  1. 1. School of Information Science and Engineering, Shandong University, Qingdao 266237, Shandong, China;
    2. Shandong Provincial Key Laboratory of Wireless Communication Technologies, Jinan 250100, Shandong, China;
    3. School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China
  • Published:2021-08-18

摘要: 针对柔性作业车间调度问题,提出一种基于狮群算法的数字孪生柔性作业车间调度方法。基于实际生产过程的需求,使用狮群算法生成柔性作业车间调度初始方案,建立物理车间与虚拟车间实时交互的数字孪生柔性作业车间调度模型,在搭建的虚拟车间中对初始调度方案根据设备利用率进行方案优化。采用数字孪生模型解决设备故障等车间突发事件对生产进程的影响问题。通过使用真实车间数据对机加工车间生产调度过程试验,结果表明,采用狮群算法求解柔性作业车间调度问题,搜寻能力强,搜索速度快,可以在不同规模的问题中找到更优的解决方案;狮群算法结合数字孪生的柔性作业车间调度方案能够整体优化系统性能,有效处理扰动带来的延长生产时间问题。

关键词: 柔性作业车间调度, 数字孪生, 狮群算法, 优化, 最大完工时间

Abstract: A digital twin job shop scheduling method based on lion swarm optimization algorithm was proposed aiming at the flexible job shop scheduling problem, which could effectively improve the utilization of equipment and solve the production delay caused by dynamic factors such as equipment failure in discrete manufacturing. The lion swarm optimization algorithm was used to generate the initial scheme based on the requirements of the actual production process. A digital twin flexible job shop scheduling model with real-time interaction between physical shop floor and virtual shop floor was established. The initial scheduling scheme was optimized according to the equipment utilization in the virtual shop floor. The digital twin model was used to solve the delay of production process caused by shop floor emergency such as equipment failure. By using real workshop data to test the machine tool production scheduling process in machining workshop, the results showed that the flexible job shop scheduling problem based on lion swarm optimization algorithm had strong search ability and fast search speed, and could find better solutions in different scale problems. The flexible job shop scheduling scheme with digital twin could optimize the system performance as a whole, and effectively deal with the problem of prolonging production time caused by disturbance.

Key words: flexible job shop scheduling, digital twin, lion swarm algorithm, optimization, makespan

中图分类号: 

  • TP391
[1] LEE J, BAGHERI B, KAO H A. A cyber-physical systems architecture for industry 4.0-based manufacturing systems[J]. Manufacturing Letters, 2015, 3:18-23.
[2] J IANG J R. An improved cyber-physical systems architecture for industry 4.0 smart factories[J]. Advances in Mechanical Engineering, 2018, 10(6): 1-15.
[3] ZHANG J, DING G, ZOU Y, et al. Review of job shop scheduling research and its new perspectives under industry 4.0[J]. Journal of Intelligent Manufacturing, 2019, 30(4):1809-1830.
[4] WU R, GUO S, LI Y, et al. Improved artificial bee colony algorithm for distributed and flexible job-shop scheduling problem[J]. Control and Decision, 2019, 34(12):2527-2536.
[5] ZHANG F, MEI Y, NGUYEN S, et al. Evolving scheduling heuristics via genetic programming with feature selection in dynamic flexible job-shop scheduling[J]. IEEE Transactions on Cybernetics, 2021, 51(4):1797-1811.
[6] MENG T, PAN Q, SANG H. A hybrid artificial bee colony algorithm for a flexible job shop scheduling problem with overlapping in operations[J]. International Journal of Production Research, 2018, 56(16): 5278-5292.
[7] WU X, LI J, SHEN X, et al. A nsga-III for solving dynamic flexible job shop scheduling problem considering deterioration effect[J]. IET Collaborative Intelligent Manufacturing, 2020, 2(4):22-33.
[8] LUO S. Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning[J]. Applied Soft Computing, 2020, 91(21):1-17.
[9] ZADEH M S, KATEBI Y, DONIAVI A. A heuristic model for dynamic flexible job shop scheduling problem considering variable processing times[J]. International Journal of Production Research, 2019, 57(910):3020-3035.
[10] ZHANG M, TAO F, NEE A Y C. Digital twin enhanced dynamic job-shop scheduling[J]. Journal of Manufacturing Systems, 2021, 58(B): 146-156.
[11] FANGY, PENG C, LOU P, et al. Digital-twin-based job shop scheduling toward smart manufacturing[J]. IEEE Transactions on Industrial Informatics, 2019, 15(12):6425-6435.
[12] CHAUDHRY I A, KHAN A A. A research survey: review of flexible job shop scheduling techniques[J]. International Transactions in Operational Research, 2015, 23(3):551-591.
[13] LIU S, YANG Y, ZHOU Y. A swarm intelligence algorithm-lion swarm optimization[J]. Pattern Recognition and Artificial Intelligence, 2018, 31(5): 431-441.
[14] LIU Q, ZHANG C, RAO Y, et al. Flexible job-shop scheduling problem with improved genetic algorithm[J]. Industrial Engineering and Management, 2009, 14(2):59-66.
[15] WEI Y. Research on improved particle swarm and its application in flexible job shop scheduling[D]. Lanzhou: School of Computer and Communication, Lanzhou University of Technology, 2020.
[16] ZHANG D, JIANG M. Parallel discrete lion swarm optimization algorithm for solving traveling salesman problem[J]. Journal of Systems Engineering and Electronics, 2020, 31(4):751-760.
[17] TAO F, ZHANG M. Digital win -floor: A New shop-floor paradigm towards smart manufacturing[J]. IEEE Access, 2017, 5:20418-20427.
[18] BRANDIMARTE P. Routing and scheduling in a flexible job shop by tabu search[J]. Annals of Operations Research, 1993, 41(3):157-183.
[19] PEZZELLA F, MORGANTI G, CIASCHETTI G. A genetic algorithm for the Flexible Job-shop Scheduling Problem[J]. Computers & Operations Research, 2008, 35(10):3202-3212.
[20] GIRISH B S, JAWAHAR N. A Particle Swarm Optimization algorithm for Flexible Job shop scheduling problem[C] //Proceeding of 5th Annual IEEE International Conference on Automation Science and Engineering. Banfalore, India: IEEE, 2009: 298-303.
[1] 丁飞,江铭炎. 基于改进狮群算法和BP神经网络模型的房价预测[J]. 山东大学学报 (工学版), 2021, 51(4): 8-16.
[2] 吴正健,木特力甫·马木提,吾尔尼沙·买买提,阿力木江·艾沙,库尔班·吾布力. 基于LTP和HOG纹理特征融合的中亚文档图像文种识别[J]. 山东大学学报 (工学版), 2021, 51(2): 115-121.
[3] 武慧虹,钱淑渠,刘衍民,徐国峰,郭本华. 精英克隆局部搜索的多目标动态环境经济调度差分进化算法[J]. 山东大学学报 (工学版), 2021, 51(1): 11-23.
[4] 祁金胜,曹洪振,石岩,杜文静,王湛. 虾米腰弯管内置导流板优化[J]. 山东大学学报 (工学版), 2020, 50(5): 64-69, 76.
[5] 杨巨成,韩书杰,毛磊,代翔子,陈亚瑞. 胶囊网络模型综述[J]. 山东大学学报 (工学版), 2019, 49(6): 1-10.
[6] 孙润稼,朱海南,刘玉田. 基于偏好多目标优化和遗传算法的输电网架重构[J]. 山东大学学报 (工学版), 2019, 49(5): 17-23.
[7] 王李龑,王飞,曹永吉,张涛,张亚新,卢奕,刘子菡. 基于两层优化的主动配电网储能优化配置[J]. 山东大学学报 (工学版), 2019, 49(5): 37-43, 51.
[8] 杨冬,王世文,王勇,陈博,郑天茹,周宁,肖天,赵雅文. 并网型风电场扩展光伏互补发电容量优化配置[J]. 山东大学学报 (工学版), 2019, 49(5): 44-51.
[9] 方波,陈红梅. 一种新的双策略进化果蝇优化算法[J]. 山东大学学报 (工学版), 2019, 49(3): 22-31.
[10] 郑店坤,许同乐,尹召杰,孟庆民. 改进PSO-BP神经网络对尾矿坝地下水位的预测方法[J]. 山东大学学报 (工学版), 2019, 49(3): 108-113.
[11] 刘洪铭,曾鸿雁,周伟,王涛. 基于改进粒子群算法作业车间调度问题的优化[J]. 山东大学学报 (工学版), 2019, 49(1): 75-82.
[12] 刘萌,徐陶阳,李常刚,吴越,王智,史方芳,苏建军,张国辉,李宽. 基于粒子群算法的受端电网紧急切负荷优化[J]. 山东大学学报 (工学版), 2019, 49(1): 120-128.
[13] 胡建平,李鑫,谢琪,李玲,张道畅. 基于Delaunay三角化的二维无约束优化EMD方法[J]. 山东大学学报 (工学版), 2018, 48(5): 9-15, 37.
[14] 钱淑渠,武慧虹,徐国峰,金晶亮. 计及排放的动态经济调度免疫克隆演化算法[J]. 山东大学学报(工学版), 2018, 48(4): 1-9.
[15] 叶明全,高凌云,万春圆. 基于人工蜂群和SVM的基因表达数据分类[J]. 山东大学学报(工学版), 2018, 48(3): 10-16.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!