Flexible Reference Model for Planning and Optimization

Generierung digitaler Fabrikmodelle mit dem digitalen Zwilling

Michael Schlecht, Jürgen Köbler, Roland de Guio
JournalIndustrie 4.0 Management
Issue Volume 37, 2021, Edition 5, Pages 53-56
Open Accesshttps://doi.org/10.30844/I40M_21-5_S53-56
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Abstract

The digital twin has moved into the focus of manufacturing companies and has been identified by Gartner as a key technology [1]. In the automotive industry, VW uses the digital twin in the cloud to plan, control and optimize production at all 122 locations in the future [2]. The digital twin is also the basis and an integral part of new, digital business models and the digitization of production companies. This article gives an overview of the current state of the art and describes a flexible reference model for planning and optimizing production systems based on the digital twin. The focus is on the one hand on the optimization of static layouts and material flows and on the other hand on the optimization of dynamic material flows and the temporal organization of processes.

Keywords

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Bibliography

[1] Gartner, Inc.: Gartner Top 10 Strategic Technology Trends for 2019. Gartner. URL: www.gartner.com/ smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2019, Abrufdatum 13.10.2020.
[2] Volkswagen: Volkswagen and Amazon Web Services to develop Industrial Cloud. URL: www.volkswagenag.com/ en/news/2019/03/volkswagen-and-amazon-web-services-to-develop-industrial-cloud.html, Abrufdatum 18.01.2021.
[3] Kritzinger, W.; Karner, M.; Traar, G.; Henjes, J.; Sihn, W.: Digital Twin in manufacturing: A categorical literature review and classification. In: IFAC-PapersOnLine 51 (2018) 11, S. 1016-1022. doi: 10.1016/j.ifacol.2018.08.474.
[4] Skoogh, A.; Perera, T.; Johansson, B.: Input data management in simulation – Industrial practices and future trends. In: Simulation Modelling Practice and Theory 29 (2012), S. 181-192. doi: 10.1016/j.simpat.2012.07.009.
[5] Reinhardt, H.; Weber, M.; Putz, M.: A Survey on Automatic Model Generation for Material Flow Simulation in Discrete Manufacturing. In: Procedia CIRP 81 (2019), S. 121-126. doi: 10.1016/j.procir.2019.03.022.
[6] Vieira, A.; Dias, L.; Santos, M.; Pereira, G.; Oliveira, J.: Setting an Industry 4.0 Research and Development Agenda for Simulation – a Literature Review. In: International Journal of Simulation Modelling 17 (2018), S. 377-390. doi: 10.2507/IJSIMM17(3)429.
[7] Straßburger, S.; Bergmann, S.; Müller-Sommer, H.: Modellgenerierung im Kontext der Digitalen Fabrik – Stand der Technik und Herausforderungen. Karlsruhe 2010.
[8] Drath, R.: Datenaustausch in der Anlagenplanung mit AutomationML: Integration von CAEX, PLCopen XML und COLLADA. Heidelberg New York 2010.

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