Digital Twin Based Real-time Production Logistics Synchronization System in a Multi-level Computing Architecture. (January 2021)

• Record Type: Journal Article
• Authors: Pan, Y.H.; Qu, T.; Wu, N.Q.; Khalgui, M.; Huang, G.Q.
• Is Part Of: Journal of manufacturing systems. Volume 58(2021)Part B
• Page Start: 246
• Page End: 260
• Publication Date: 2021-01
• Subjects: Digital Twin -- Production logistics -- Synchronization -- Cloud computing -- Fog computing -- Edge computing; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2020.10.015
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: Dynamics are divided into three levels according to the range of impact. A multi-level cloud computing structure enabled digital twin is proposed to deal with dynamics. Re-understanding the DT from four dimensions, entity, control strategy, computing and optimizer. The double-deck collaborative optimization algorithm is used for PL system optimization. Abstract: The increasing demands for customized products have brought a lot of inevitable operational dynamics to the production logistics system. How to systematically monitor and evaluate its overall real-time operation status, and to invoke the most appropriate decision-making level, use the most economical computational resource, and conduct a synchronized decision-making and control to the most accurate operation scope to address a randomly occurred dynamics has remained a long-term challenge for researchers. This paper proposes a multi-level cloud computing enabled digital twin system for the real-time monitor, decision and control of a synchronized production logistics system. In the IoT-driven production logistics synchronization (PLS) system with complete real-time information, the dynamics that occurred in the physical layer could be accurately and real-timely captured and its negative effects on the system's overall operation state could be effectively evaluated in the digital layer. For slight, moderate and severe dynamics, edge computing, fog computing and cloud computing and their dynamically formed multi-level distributed decision-making system will be used to deal with the dynamics in the most effective and economical mode. Finally, the PLS optimization model of production and storage is presented with an industrial case, and the effectiveness is also demonstrated and analyzed.