Journal of Shandong University(Engineering Science) ›› 2021, Vol. 51 ›› Issue (4): 17-23.doi: 10.6040/j.issn.1672-3961.0.2021.091

Previous Articles    

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

PDF (PC)

305

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

CLC Number: 

  • 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] DING Fei, JIANG Mingyan. Housing price prediction based on improved lion swarm algorithm and BP neural network model [J]. Journal of Shandong University(Engineering Science), 2021, 51(4): 8-16.
[2] WU Zhengjian, MUTALLIP Mamut, HORNISA Mamat, ALIM Aysa, KURBAN Ubul. Script identification of Central Asian document images based on LTP and HOG texture feature fusion [J]. Journal of Shandong University(Engineering Science), 2021, 51(2): 115-121.
[3] WU Huihong, QIAN Shuqu, LIU Yanmin, XU Guofeng, GUO Benhua. Multiobjective dynamic economic emission dispatch differential evolution algorithm based on elites cloning local search [J]. Journal of Shandong University(Engineering Science), 2021, 51(1): 11-23.
[4] Jinsheng QI,Hongzhen CAO,Yan SHI,Wenjing DU,Zhan WANG. Optimization of the inner deflector of the shrimp-waist elbow [J]. Journal of Shandong University(Engineering Science), 2020, 50(5): 64-69, 76.
[5] Runjia SUN,Hainan ZHU,Yutian LIU. Transmission network reconfiguration strategy based on preference multiobjective optimization and genetic algorithm [J]. Journal of Shandong University(Engineering Science), 2019, 49(5): 17-23.
[6] Liyan WANG,Fei WANG,Yongji CAO,Tao ZHANG,Yaxin ZHANG,Yi LU,Zihan LIU. Bi-level optimal configuration of energy storage system in an active distribution network [J]. Journal of Shandong University(Engineering Science), 2019, 49(5): 37-43, 51.
[7] Dong YANG,Shiwen WANG,Yong WANG,Bo CHEN,Tianru ZHENG,Ning ZHOU,Tian XIAO,Yawen ZHAO. Optimal complementary photovoltaic capacity configuration for grid-connected wind farms expansion [J]. Journal of Shandong University(Engineering Science), 2019, 49(5): 44-51.
[8] Bo FANG,Hongmei CHEN. A novel double strategies evolutionary fruit fly optimization algorithm [J]. Journal of Shandong University(Engineering Science), 2019, 49(3): 22-31.
[9] Diankun ZHENG,Tongle XU,Zhaojie YIN,Qingmin MENG. Prediction method of tailing dam groundwater levels based on improved PSO-BP neural network [J]. Journal of Shandong University(Engineering Science), 2019, 49(3): 108-113.
[10] Xiaoqiang ZHU,Maiying ZHONG. Fault detection for unmanned aerial vehicle systems based on strong tracking H-/H∞ optimization [J]. Journal of Shandong University(Engineering Science), 2019, 49(1): 66-74.
[11] Hongming LIU,Hongyan ZENG,Wei ZHOU,Tao WANG. Optimization of job shop scheduling based on improved particle swarm optimization algorithm [J]. Journal of Shandong University(Engineering Science), 2019, 49(1): 75-82.
[12] Meng LIU,Taoyang XU,Changgang LI,Yue WU,Zhi WANG,Fangfang SHI,Jianjun SU,Guohui ZHANG,Kuan LI. Optimization of emergency load shedding of receiving-end power grid based on Particle Swarm Optimization [J]. Journal of Shandong University(Engineering Science), 2019, 49(1): 120-128.
[13] Xiaoyan GONGYE,Peiguang LIN,Weilong REN. Genetic algorithm based on Grefenstette coding and 2-opt optimized [J]. Journal of Shandong University(Engineering Science), 2018, 48(6): 19-26.
[14] Jianping HU,Xin LI,Qi XIE,Ling LI,Daochang ZHANG. An unconstrained optimization EMD approach in 2D based on Delaunay triangulation [J]. Journal of Shandong University(Engineering Science), 2018, 48(5): 9-15, 37.
[15] QIAN Shuqu, WU Huihong, XU Guofeng, JIN Jingliang. Immune clonal evolutionary algorithm of dynamic economic dispatch considering gas pollution emission [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(4): 1-9.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of Journal of Shandong University (Engineering Science)
Supported by Beijing Magtech Co.Ltd