中国邮电高校学报(英文版) ›› 2022, Vol. 29 ›› Issue (2): 108-120.doi: 10. 19682/ j. cnki. 1005-8885. 2021. 0022
• Others • 上一篇
With the application of various information technologies in smart manufacturing, new intelligent production mode puts forward higher demands for real-time and robustness of production scheduling. For the production scheduling problem in large-scale manufacturing environment, digital twin (DT) places high demand on data processing capability of the terminals. It requires both global prediction and rea-time response abilities. In order to solve the above problem, a DT-based edge-cloud collaborative intelligent production scheduling (DTECCS) system was proposed, and the scheduling model and method were introduced. DT-based edge-cloud collaboration (ECC) can predict the production capacity of each workshop, reassemble customer orders, optimize the allocation of global manufacturing resources in the cloud, and carry out distributed scheduling on the edge-side to improve scheduling and tasks processing efficiency. In the production process, the DTECCS system adjusts scheduling strategies in real-time, responding to changes in production conditions and order fluctuations. Finally, simulation results show the effectiveness of DTECCS system.
 WEI S, MA Y Y, LI R Q, et al. Toward smart manufacturing: key technologies and trends driving standardization. Computer, 2020, 53(4): 46 -50.
 TAO F, QI Q L. New IT driven service-oriented smart manufacturing: framework and characteristics. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49 (1): 81 -91.
 CHENG G J, LIU L T, QIANG X J, et al. Industry 4.0 development and application of intelligent manufacturing. Proceedings of the 2016 International Conference on Information System and Artificial Intelligence (ISAI ‘16), Jun 24 -26, 2016, Hong Kong, China. Piscataway, NJ, USA: IEEE, 2016: 407 -410.
 RAZA M, KUMAR P M, HUNG D V, et al. A digital twin framework for industry 4.0 enabling next-gen manufacturing.
Proceedings of the 9th International Conference on Industrial Technology and Management (ICITM ‘20), Feb 11 - 13, 2020, Oxford, UK. Piscataway, NJ, USA: IEEE, 2020: 73 -77.
 WU P F, QI M J, GAO L Y, et al. Research on the virtual reality synchronization of workshop digital twin. Proceedings of the IEEE 8th Joint International Information Technology and Artificial Intelligence Conference ( ITAIC ‘19 ), May 24 - 26, 2019, Chongqing, China. Piscataway, NJ, USA: IEEE, 2019: 875 -
 ZHAO M. Exploration of the root and in-depth application of digital twin. Software and Integrated Circuit, 2018, (9): 50 -58 (in Chinese).
 ZHANG H J, ZHANG G H, YAN Q. Dynamic resource allocation optimization for digital twin-driven smart shopfloor. Proceedings of the IEEE 15th International Conference on Networking, Sensing
and Control (ICNSC ‘18), Mar 27 - 29, 2018, Zhuhai, China. Piscataway, NJ, USA: IEEE, 2018: 1 -8.
 HUANG M, WANG F. A new multi-agent model of workshop scheduling. Proceedings of the IEEE 3rd International Conference on Cloud Computing and Internet of Things ( CCIOT’18), Oct 20 -21, 2018, Dalian, China. Piscataway, NJ, USA: IEEE, 2018: 25 -29.
 ARENA F, PAU G. When edge computing meets IoT systems: analysis of case studies. China Communications, 2020, 17(10): 55 -68.
 JIANG S H, SHU H, REN Y, et al. Analysis on platform and application of edge computing. Software Guide, 2020, 19(10): 205 -208 (in Chinese).
 PANCHALI B. Edge computing-background and overview. Proceedings of the 2018 International Conference on Smart Systems and Inventive Technology ( ICSSIT’18), Dec 13 - 14, 2018, Tirunelveli, India. Piscataway, NJ, USA: IEEE, 2018: 580 -582.
 PREMSANKAR G, DI FRANCESCO M, TALEB T. Edge computing for the Internet of things: a case study. IEEE Internet of Things Journal, 2018, 5(2): 1275 -1284.
 GEORGAKOPOULOS D, JAYARAMAN P P, FAZIA M, et al. Internet of things and edge cloud computing roadmap for manufacturing. IEEE Cloud Computing, 2016, 3(4): 66 -73.
 TAO F, ZHANG M. Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access, 2017, 5: 20418 -20427.
 TAO F, CHENG Y, CHENG J F, et al. Theories and technologies for cyber-physical fusion in digital twin shop-floor. Computer Integrated Manufacturing Systems, 2017, 23 (8): 1603 – 1611 (in Chinese).
 TAO F, CHENG J F, QI Q L, et al. Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 2018, 94(9/10/11/12): 3563 -3576.
 LEE J, AZAMFAR M, SINGH J, et al. Integration of digital twin and deep learning in cyber-physical systems: towards smart manufacturing. IET Collaborative Intelligent Manufacturing, 2020, 2(1): 34 -36.
 GRAESSLER I, POEHLER A. Integration of a digital twin as human representation in a scheduling procedure of a cyber-physical production system. Proceedings of the 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM’17), Dec 10 - 13, 2017, Singapore. Piscataway, NJ, USA: IEEE, 2017: 289 -293.
 LIU Z F, CHEN W, YANG C B, et al. Intelligent manufacturing workshop dispatching cloud platform based on digital twins. Computer Integrated Manufacturing Systems, 2019, 25 ( 6 ): 1444 -1453 (in Chinese).
 FANG Y L, PENG C, LOU P, et al. Digital-twin based job shop scheduling towards smart manufacturing. IEEE Transactions on Industrial Informatics, 2019, 15(12): 6425 -6435.
 ZHOU L F, ZHANG L. A dynamic task scheduling method based on simulation in cloud manufacturing. Proceedings of the 16th Asia Simulation Conference and SCS Autumn Simulation Multi-Conference ( AsiaSim/ SCS AutumnSim ‘16 ): Part Ⅲ, Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems, Oct 8 - 11, 2016, Beijing, China. Berlin, Germany: Springer, 2016: 20 -24.
 LU P H, WU M C, TAN H, et al. A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems. Journal of Intelligent Manufacturing, 2018, 29(1): 19 -34.
 QI Q L, TAO F. A smart manufacturing service system based on edge computing, fog computing and cloud computing. IEEE Access, 2019, 7: 86769 -86777.
 AFRIN M, JIN J, RAHMAN A, et al. Multi-objective resource allocation for edge cloud based robotic workflow in smart factory. Future Generation Computer Systems, 2019, 97: 119 -130.
 MA J, ZHOU H, LIU C C, et al. Study on edge-cloud collaborative production scheduling based on enterprises with
multi-factory. IEEE Access, 2020, 8: 30069 -30080.
 WU X L. Solving the flexible job-shop scheduling problem with quantum-inspired algorithm. Proceedings of the 10th World Congress on Intelligent Control and Automation, Jul 6 -8, 2012, Beijing, China. Piscataway, NJ, USA: IEEE, 2012: 538 -543.
 LI D W. To solve the job shop scheduling problem with the improve quantum genetic algorithm. Proceedings of the 3rd Global Congress on Intelligent Systems, Nov 6 - 8, 2012, Wuhan, China. Piscataway, NJ, USA: IEEE, 2012: 88 -91.
 DRISS I, MOUSS K N, LAGGOUN A. A new genetic algorithm for flexible job-shop scheduling problems. Journal of MechanicalScience and Technology, 2015, 29(3): 1273 -1281.
 ANSHULIKA, BEWOOR L A. A genetic algorithm approach for solving a job shop scheduling problem. Proceedings of the 2017 International Conference on Computer Communication and Informatics (ICCCI ‘17), Jan 5 - 7, 2017, Coimbatore, India. Piscataway, NJ, USA: IEEE, 2017: 1 -6.
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