Hybrid system modeling approach for the depiction of the energy consumption in production simulations. - In: 2019 Winter Simulation Conference (WSC), (2019), S. 1366-1377
In many industrial manufacturing companies, energy has become a major cost factor. Energy aspects are included in the decision-making system of production planning and control to reduce manufacturing costs. For this priority, the simulation of production processes requires not only the consideration of logistical and technical production factors but also the integration of time-dependent energy flows which are continuous in nature. A hybrid simulation, using a continuous approach to depict the energy demand of production processes in combination with a discrete approach to map the material flows and logistic processes, shows the complex interactions between material flow and energy usage in production closer to reality. This paper presents a hybrid simulation approach combining System Dynamics, Discrete-Event and Agent-Based Simulation for energy efficiency analysis in production, considering the energy consumption in the context of planning and scheduling operations and applying it to a use-case scenario of mechanical processing of die-cast parts.
Hybrid simulation development - is it just analytics?. - In: 2019 Winter Simulation Conference (WSC), (2019), S. 1352-1365
Richtiger Name des 5. Verfassers: Steffen Straßburger
Hybrid simulations can take many forms, often connecting a diverse range of hardware and software components with heterogeneous data sets. The scale of examples is also diverse with both the high-performance computing community using high-performance dataanalytics (HPDA) to the synthesis of software libraries or packages on a single machine. Hybrid simulation configuration and output analysis is often akin to analytics with a range of dashboards, machine learning, data aggregations and graphical representation. Underpinning the visual elements are hardware, software and data architectures that execute hybrid simulation code. These are wide ranging with few generalized blueprints, methods or patterns of development. This panel will discuss a range of hybrid simulation development approaches and endeavor to uncover possible strategies for supporting the development and coupling of hybrid simulations.
Modelling and simulation of modular production systems :
Modellierung und Simulation von modularen Produktionssystemen. - In: Simulation in Produktion und Logistik 2019, (2019), S. 391-401
Modular production systems aim to supersede the traditional line production in the automobile industry. The idea here is that highly customized products can move dynamically and autonomously through a system of flexible workstations without fixed production cycles. This approach has challenging demands regarding planning and organization of such systems. The use of modelling and simulation methods is therefore indispensable. This paper presents simulation approaches for modelling modular production systems and discusses a comparison between an agent-based and a process-oriented implementation of an example model.
Deep learning based prediction of energy consumption for hybrid simulation :
Deep-Learning-basierte Prognose von Stromverbrauch für die hybride Simulation. - In: Simulation in Produktion und Logistik 2019, (2019), S. 121-131
Modern production facilities need to prepare for changing market conditions within the energy market due to ongoing implementation of governmental policies. This results in higher volatility of the availability of energy and therefore energy costs. If a simulation model of a machinery model can estimate its own future consumption, and according time frames for said consumption, this information could be used for optimized scheduling of energy consuming jobs. This would result in lower procurement costs. To make said estimation about the dynamic behaviour of jobs, methods of time series prediction tend to be applied. Here a proposal is made to apply a Hybrid System Model incorporating a recurrent neural network (RNN)-Encoder-Decoder-Architecture, which returns a discrete times series when a behavioural sequence (such as an NC-Code) has been put into a neural net model of the respective machinery. Those discrete time series reflect the machines energy consumption for each job that it has been operated on. This neural net, if weighted and called, emits the length value of a job and an according time series which displays the quasi-continuous time consumption of said job. Such generative models combined with classic simulation paradigm qualify as potent applications of hybrid simulation approaches.
Knowledge discovery and robustness analysis for simulation models of global networks :
Wissensentdeckung und Robustheitsanalyse für Simulationsmodelle weltweiter Netze, (2019), S. 64-76
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.
Knowledge discovery in scheduling systems using evolutionary bilevel optimization and visual analytics. - In: Evolutionary multi-criterion optimization, (2019), S. 439-450
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.
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.
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.