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Applying model-reconstruction by exploring MES and PLC data for simulation support of production systems

: Pfeiffer, András; Kádár, Botond; Popovics, Gergely; Kardos, Csaba; Vén, Zoltán; Kemény, Lorinc; Monostori, László

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Laroque, C. ; Institute of Electrical and Electronics Engineers -IEEE-; American Statistical Association -ASA-; Association for Computing Machinery -ACM-, Special Interest Group on Simulation; IEEE Systems, Man and Cybernetics Society -SMC-:
Winter Simulation Conference, WSC 2012. Proceedings. Vol.3 : Berlin, Germany, 9 - 12 December 2012; including the 8th International Conference on Modeling and Analysis of Semiconductor Manufacturing (MASM)
New York, NY: IEEE, 2012
ISBN: 978-1-4673-4779-2 (Print)
ISBN: 978-1-4673-4781-5 (Online)
Winter Simulation Conference (WSC) <2012, Berlin>
International Conference on Modeling and Analysis of Semiconductor Manufacturing (MASM) <8, 2012, Berlin>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPA ()
discrete event simulation (DES); Manufacturing Execution System (MES); production system; Programmable Logic Controllers (PLC); Fertigungssystem; Simulation

The paper introduces a discrete-event simulation-based decision supporting system aiming at automatically mirroring the current state of a large-scale material handling system of a production system in a digital model and supporting the analysis of diverse control settings and rules. The discrete-event digital model is built in an automated way and all the data necessary for the model are taken from a manufacturing execution system (MES) and additionally directly from programmable logic controllers (PLC). Main focus is given to present the results of the PLC program code processing method (code mapping) generating a structured dataset, as well as the model-reconstruction method for the simulation software. The easy-of-use support tool is intended to be used both in planning and operation phases of an automotive manufacturing company, thus the capabilities of model reconstruction and simulation are tested on real-world data.