Knowledge discovery in simulation data. - In: ACM transactions on modeling and computer simulation, ISSN 1558-1195, Bd. 30 (2020), 4, S. 24:1-24:25
This article provides a comprehensive and in-depth overview of our work on knowledge discovery in simulations. Application-wise, we focus on manufacturing simulations. Specifically, we propose and discuss a methodology for designing, executing, and analyzing large-scale simulation experiments with a broad coverage of possible system behavior targeted at generating knowledge about the system. Based on the concept of data farming, we suggest a two-phase process which starts with a data generation phase, in which a smart experiment design is used to set up and efficiently execute a large number of simulation experiments. In the second phase, the knowledge discovery phase, data mining and visually aided analysis methods are applied on the gathered simulation input and output data. This article gives insights into this knowledge discovery phase by discussing different machine learning approaches and their suitability for different manufacturing simulation problems. With this, we provide guidelines on how to conduct knowledge discovery studies within the manufacturing simulation context. We also introduce different case studies, both academic and applied, and use them to validate our methodology.
https://doi.org/10.1145/3391299
A method for robustness optimization using generative adversarial networks. - In: SIGSIM-PADS '20, (2020), S. 1-10
This paper presents an approach for optimizing the robustness of production and logistic systems based on deep generative models, a special method of deep learning. Robustness here refers to setting controllable factors of a system in such a way that variance in the uncontrollable factors (noise) has a minimal effect on given output parameters. In a case study, the proposed method is tested and compared to a traditional method for robustness analysis. The basic idea is to use deep neural networks to generate data for experiment plans and rate them by use of a simulation model of the production system. We propose to use two Generative Adversarial Networks (GANs) to generate optimized experiment plans for the decision factors and the noise factors, respectively, in a competitive, turn-based game. In one turn, the controllable factors are optimized and the noise remains constant, and vice versa in the next turn. For the calculations of the robustness, the planned experiments are conducted and rated using a simulation model in each learning step.
https://doi.org/10.1145/3384441.3395981
Wissensentdeckung im Kontext der Produktionssimulation. - Ilmenau : Universitätsverlag Ilmenau, 2020. - 1 Online-Ressource (XII, 217, XIV-XX Seiten)
Technische Universität Ilmenau, Dissertation 2019
Die diskrete Simulation stellt eine wichtige und etablierte Methode zur Untersuchung des dynamischen Verhaltens von komplexen Produktions- und Logistiksystemen dar. Sie ist daher zur Planung, Steuerung und Kontrolle solcher Systeme unerlässlich, beispielsweise in der Automobilindustrie oder in der Halbleiterfertigung. Klassische Simulationsstudien zielen in diesem Kontext üblicherweise darauf ab, typische, vorab definierte Fragestellungen zu beantworten. Dies geht oftmals einher mit der Simulation und Analyse einiger weniger vorab definierter Szenarien. Wirkzusammenhänge, die über diesen definierten Projektrahmen hinausgehen, bleiben daher eventuell unentdeckt. Auf der anderen Seite erwachsen mit steigender Rechenleistung und der allgemeinen Verfügbarkeit von Big-Data-Infrastrukturen neue Möglichkeiten zur Durchführung von sehr großen Bandbreiten von Simulationsexperimenten, um das Verhalten des Modells möglichst vollständig abzudecken und automatisiert auszuwerten. Dies wird allgemein als Data Farming bezeichnet. Ziel dieser Arbeit war es, die Methode des Data Farming für die Nutzung zur Wissensentdeckung in Produktionssimulationen zu übertragen und weiterzuentwickeln. Dazu wurde ein ganzheitliches Konzept ausgearbeitet, um unbekannte, versteckte und potenziell nützliche Wirkzusammenhänge in großen Mengen von Simulationsdaten entdecken zu können. Das Konzept beinhaltet hierzu die Auswahl geeigneter Experimentdesignmethoden, die Anwendung und Ausgestaltung von geeigneten Data-Mining-Verfahren in einem dafür zweckmäßigen und zielgerichteten Analyseprozess sowie die Definition geeigneter Visualisierungs- und Interaktionsmethoden zur iterativen, anwenderorientierten Analyse großer Mengen von Simulationsdaten. Darüber hinaus wurde das Konzept in einem ganzheitlichen Softwareframework prototypisch implementiert. Die Anwendbarkeit des Konzeptes wurde anhand von vier Fallstudien aufgezeigt und validiert. Die Fallstudien beinhalteten hierbei zwei akademische Laborstudien sowie zwei Industrieanwendungsfälle.
https://www.db-thueringen.de/receive/dbt_mods_00040526
Visualization and interaction for knowledge discovery in simulation data. - In: Hawaii International Conference on System Sciences 2020, (2020), S. 1340-1349
Discrete-event simulation is an established and popular technology for investigating the dynamic behavior of complex manufacturing and logistics systems. Besides traditional simulation studies that focus on single model aspects, data farming describes an approach for using the simulation model as a data generator for broad scale experimentation with a broader coverage of the system behavior. On top of that we developed a process called knowledge discovery in simulation data that enhances the data farming concept by using data mining methods for the data analysis. In order to uncover patterns and causal relationships in the model, a visually guided analysis then enables an exploratory data analysis. While our previous work mainly focused on the application of suitable data mining methods, we address suitable visualization and interaction methods in this paper. We present those in a conceptual framework followed by an exemplary demonstration in an academic case study.
https://doi.org/10.24251/HICSS.2020.165
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
http://ceur-ws.org/Vol-2397/paper9.pdf
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
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.