Augmented Reality in intralogistics planning of the automotive industry. - In: 2019 IEEE 6th International Conference on Industrial Engineering and Applications, (2019), S. 203-208
This article investigates Augmented Reality (AR) as a potential tool to support intralogistics planning in the automotive industry. Starting with a literature review and an investigation of the dissemination of AR usage in logistics in general, we analyze potential reasons for the apparent lack of AR applications in intralogistics planning. From this, we derive requirements for a successful application of AR in intralogistics planning and demonstrate a prototypical solution implemented within the Daimler AG. Based on this example, we further discuss the advantages of applying AR to intralogistics planning.
https://doi.org/10.1109/IEA.2019.8714848
Knowledge discovery in scheduling systems using evolutionary bilevel optimization and visual analytics. - In: Evolutionary multi-criterion optimization, (2019), S. 439-450
https://doi.org/10.1007/978-3-030-12598-1_35
On the role of simulation and simulation standards in Industry 4.0. - Ilmenau : Universitätsbibliothek. - 1 Online-Ressource (12 Seiten)Publikation entstand im Rahmen der Veranstaltung: 2019 Simulation Innovation Workshop (SIW), February 11-15, 2019, Florida Hotel & Conference Center at the Florida Mall, Orlando, FL., Paper 2019-SIW-06
This article introduces the concepts and ideas behind Industry 4.0 and discusses the role of simulation and simulation standards for implementing it. We argue that the success of Industry 4.0 highly depends on the success of interconnected cyber-physical systems (CPS) which can only be implemented with up-front simulation. This up-front simulation and development of CPS is often associated with the term of building the "digital twin" for the respective CPS. Digital twins are typically defined as digital representations which represent the real system and its current state in a digital model. For investigating their dynamic behavior, digital twins must have properties typically associated with simulation models. In this article, we discuss requirements and potential solutions for the successful implementation of digital twins as well as the implications that this has on simulation standards. As an example, digital twins as representations of a CPS will have the need to communicate with other digital twins; hence a modular approach for building federations of digital twins is needed. Beyond that, also a need for standardized communication between the digital twin and the real CPS arises. The article will therefore discuss currently available interoperability standards, like the High Level Architecture (HLA) on the simulation side, and Open Platform Communications (OPC) Unified Architecture (OPC UA) on the control hardware side and how well they match the requirements that Industry 4.0 with its CPSs and digital twins imposes. The article also includes our opinion on the need for the future evolvement of existing standards.
https://doi.org/10.22032/dbt.38300
Combining data farming and data envelopment analysis for measuring productive efficiency in manufacturing simulations. - In: Simulation for a noble cause, (2018), S. 1440-1451
Discrete event simulation is an established methodology for investigating the dynamic behavior of complex manufacturing and logistics systems. In addition to traditional simulation studies, the concept of data farming and knowledge discovery in simulation data is a current research topic that consist of broad scale experimentation and data mining assisted analysis of massive simulation output data. While most of the current research aims to investigate key drivers of production performance, in this paper we propose a methodology for investigating productive efficiency. We therefore developed a concept of combining our existing approach of data farming and visual analytics with data envelopment analysis (DEA), which is used to investigate efficiency in operations research and economics. With this combination of concepts, we are not only able to determine key factors and interactions that drive productive efficiency in the modeled manufacturing system, but also to identify the most productive settings.
https://doi.org/10.1109/WSC.2018.8632300
Data Farming und simulationsbasierte Robustheitsanalyse für Fertigungssysteme. - In: ASIM 2018 - 24. Symposium Simulationstechnik, (2018), S. 243-252
Diskrete Simulation ist eine etablierte Methodik zur Untersuchung des dynamischen Verhaltens von komplexen Fertigungs- und Logistiksystemen. Konventionelle Simulationsstudien fokussieren auf einzelne Modellaspekte und spezifische Analysefragen. Der Umfang der ausgeführten Szenarien ist häufig gering. Das Konzept des Data-Farming verwendet das Simulationsmodell als Datengenerator für eine breite Skale von Experimenten und ermöglicht unter Nutzung von Data-Mining-Methoden eine wesentlich breitere Untersuchung des simulierten Systems sowie eine höhere Komplexität in den abgeleiteten Erkenntnissen. Anforderungen an Simulationssysteme und -modelle zur Durchführung von Data-Farming werden erläutert. Eine Erweiterung des Ansatzes ist die simulationsbasierte Robustheitsanalyse auf der Basis von Verlustfunktionen nach Taguchi. Beide Vorgehensweisen werden an einer Fallstudie aus dem Fahrzeugbau demonstriert.
Eignung kombinierter Simulation zur Darstellung energetischer Aspekte in der Produktionssimulation. - In: ASIM 2018 - 24. Symposium Simulationstechnik, (2018), S. 73-80
In vielen produzierenden Unternehmen ist Energie ein wesentlicher Kostenfaktor. Energieaspekte werden deshalb in das Entscheidungssystem der Produktionsplanung und -steuerung einbezogen, um die Herstellungskosten zu senken. Die Simulation von Produktionsprozessen erfordert neben der Berücksichtigung technischer und logistischer Produktionsfaktoren auch die Integration von kontinuierlichen Energieverbräuchen. Da Fertigungssysteme im Allgemeinen in diskreten Simulationsmodellen beschrieben werden, könnte ein Ansatz, der die beiden Systemdynamiken kombiniert, vorteilhaft sein. Die kombinierte Simulation nutzt einen kontinuierlichen Simulationsansatz zur Abbildung des Energiebedarfs relevanter Produktionsprozesse und kombiniert diesen mit einem diskreten Simulationsansatz zur Abbildung von Material- und Logistikprozessen. Durch die Zusammenführung der Modelle können die Wechselwirkungen zwischen Materialfluss und Energieverbrauch in der Produktion realitätsnäher simuliert werden.
Bilevel innovization: knowledge discovery in scheduling systems using evolutionary bilevel optimization and visual analytics. - In: GECCO'18 companion, ISBN 978-1-4503-5764-7, (2018), S. 197-198
https://doi.org/10.1145/3205651.3205726
Online analysis of simulation data with stream-based data mining. - In: SIGSIM-PADS'17, (2017), S. 241-248
https://doi.org/10.1145/3064911.3064915
Knowledge discovery in simulation data - a case study for a backhoe assembly line. - In: WSC'17, ISBN 978-1-5386-3428-8, (2017), S. 4456-4458
Discrete event simulation is an established and popular technology for investigating the dynamic behavior of complex manufacturing and logistics systems. Besides conventional simulation studies that focus on single model aspects answering project specific analysis questions, new methods of broad scale experiment design and system analysis emerge alongside new developments of computational power and data processing. This enables to investigate the bandwidth of possible system behavior in a more in-depth way. In this work we applied our previously developed methodology of knowledge discovery in simulation data onto an industrial case study for a backhoe loader manufacturing facility.
https://doi.org/10.1109/WSC.2017.8248162
Knowledge discovery and robustness analysis in manufacturing simulations. - In: WSC'17, ISBN 978-1-5386-3428-8, (2017), S. 3952-3963
Discrete event simulation is an established methodology for investigating the dynamic behavior of complex manufacturing and logistics systems. Traditionally, simulation experts conduct experiments for predetermined system specifications focusing on single model aspects and specific analysis questions. In addition to that, the concept of data farming and knowledge discovery is an ongoing research issue that consists of broad scale experimentation and data mining assisted analysis of massive simulation output data. As an extension to this approach, we propose a concept for investigating the robustness of complex manufacturing and logistic systems which are often very sensitive to variation and noise. Based on Taguchis loss function, we developed a concept including data farming and visual analytics methodologies to investigate sources of variation in a model and the factor values that make a configuration robust. The concept is demonstrated on an exemplary case study model.
https://doi.org/10.1109/WSC.2017.8248105