Publications - Complete List as of 2007 (without theses)

Results: 119
Created on: Sun, 24 Sep 2023 13:44:13 +0200 in 0.0601 sec

Feldkamp, Niclas; Straßburger, Steffen
From explainable AI to explainable simulation: using machine learning and XAI to understand system robustness. - In: ACM SIGSIM-PADS 2023, (2023), S. 96-106

Evaluating robustness is an important goal in simulation-based analysis. Robustness is achieved when the controllable factors of a system are adjusted in such a way that any possible variance in uncontrollable factors (noise) has minimal impact on the variance of the desired output. The optimization of system robustness using simulation is a dedicated and well-established research direction. However, once a simulation model is available, there is a lot of potential to learn more about the inherent relationships in the system, especially regarding its robustness. Data farming offers the possibility to explore large design spaces using smart experiment design, high performance computing, automated analysis, and interactive visualization. Sophisticated machine learning methods excel at recognizing and modelling the relation between large amounts of simulation input and output data. However, investigating and analyzing this modelled relationship can be very difficult, since most modern machine learning methods like neural networks or random forests are opaque black boxes. Explainable Artificial Intelligence (XAI) can help to peak into this black box, helping us to explore and learn about relations between simulation input and output. In this paper, we introduce a concept for using Data Farming, machine learning and XAI to investigate and understand system robustness of a given simulation model.
Wörrlein, Benjamin; Straßburger, Steffen
Dynamic Time Warping und Synthesedaten zur Validierung von Seq2Seq für die Simulation. - In: ASIM Workshop 2023, (2023), S. 133-142

Seq2Seq is a machine learning method that allows to translate sequences into other sequences. This method has been tried in hybrid simulation of machine tools. The method has been used to generate time series of energy consumption of jobs from the corresponding numerical control code that runs on a machine tool. Seq2Seq suffers from various problems. Firstly, the creation of training data is costly. Secondly, standard Seq2Seq metrics only allow for the evaluation of a prediction of one timestamp at a time, not an entire time series. Thirdly, training metrics are failing when vanilla data is used, as two identical numerical control codes can result in deviating time series. This causes confusion for the model in the training loop, as it is not clear which time series should be considered correct. Here we propose a holistic framework to all three problems, that contains synthetic data, additional metrics for time series and dynamic time warping.
Scheer, Richard; Straßburger, Steffen; Knapp, Marc
Hybridization of the Digital Twin - overcoming implementation challenges. - In: Proceedings of the 56th Annual Hawaii International Conference on System Sciences, (2023), S. 1438-1447

In the context of Industry 4.0 the concept of the Digital Twin has gained significant momentum in industry as well as academia. Researchers have hypothesized a great number of potential benefits of the concept's usage. However, few real-world implementations have been recorded. This paper addresses the most pressing challenges inhibiting the concept's industrial application. It describes the process of the concept's hybridization to achieve a practical implementation strategy: the Hybrid Digital Twin. Subsequently, a prototype is implemented using a presently operational real-world manufacturing system to substantiate the viability of the methodology. Finally, the benefits, remaining issues and future developments of the concept are discussed.
Morlang, Frank; Straßburger, Steffen
On the role of HLA-based simulation in New Space. - In: 2022 Winter Simulation Conference (WSC), (2022), S. 430-440

This paper discusses High Level Architecture (HLA) based simulation in the context of the emergence of the private spaceflight industry called New Space. We postulate that distributed simulation plays a fundamental role in facilitating new opportunities of a cost efficient access to space. HLA defines a simulation system's architecture framework with a focus on reusability and interoperability. The article will therefore discuss the impact of its usage on the potential of affordable new aerospace systems developments. Future possibilities with an increased level of loose component coupling are presented.
Bergmann, Sören;
Optimization of the design of modular production systems. - In: 2022 Winter Simulation Conference (WSC), (2022), S. 1783-1793

The desire for more flexibility in manufacturing systems, especially when different products or many product variants are manufactured in one production system is leading to a move away from the manufacturing principle of classic line production to more flexible and workshop-oriented production systems, particularly in the automotive industry. One of the challenges in these so-called modular assembly or production systems is the system design, especially the allocation of activities to the individual production cells. One approach to improve this allocation is offered by simulation-based optimization. In this paper, a concept for simulation-based optimization of the design of modular production systems is presented and demonstrated by means of a small academic case study. Classical genetic algorithms and additionally the NSGA-II algorithm, which also allows multi-objective optimization, are used.
Feldkamp, Niclas; Genath, Jonas; Straßburger, Steffen
Explainable AI for data farming output analysis: a use case for knowledge generation through black-box classifiers. - In: 2022 Winter Simulation Conference (WSC), (2022), S. 1152-1163

Data farming combines large-scale simulation experiments with high performance computing and sophisticated big data analysis methods. The portfolio of analysis methods for those large amounts of simulation data still yields potential to further development, and new methods emerge frequently. Among the most interesting are methods of explainable artificial intelligence (XAI). Those methods enable the use of black-box-classifiers for data farming output analysis, which has been shown in a previous paper. In this paper, we apply the concept for XAI-based data farming analysis on a complex, real world case study to investigate the suitability of such concept in a real world application, and we also elaborate on which black-box classifiers are actually the most suitable for large-scale simulation data that accumulates in a data farming project.
Genath, Jonas; Bergmann, Sören; Feldkamp, Niclas; Spieckermann, Sven; Stauber, Stephan
Development of an integrated solution for data farming and knowledge discovery in simulation data. - In: Simulation Notes Europe, ISSN 2164-5353, Bd. 32 (2022), 3, S. 121-126

Simulation is an established methodology for planning and evaluating manufacturing and logistics systems. In contrast to classical simulation studies, the method of knowledge discovery in simulation data uses a simulation model as a data generator (data farming). Subsequently, hidden, previously unknown and potentially useful cause-effect relationships can be uncovered on the generated data using data mining and visual analytics methods. So far, however, there was a lack of integrated, easy-to-use software solutions for the application of the data farming in operational practice. This paper presents such an integrated solution, which allows generating experiment designs, implements a method to distribute the necessary experiment runs, and provides the user with tools to analyze and visualize the result data.
Straßburger, Steffen;
Die ereignisdiskrete Simulation und ihr Verhältnis zu informationstechnischen Modetrends. - In: Drei Dutzend Jahre Simulationstechnik, (2022), S. 5-6

Dem Motto dieses Festkolloquiums entsprechend schaut dieser Beitrag auf die Methode der ereignisdiskreten Simulation und ihr Verhältnis zu informationstechnischen Modetrends im Betrachtungszeitraum. Im Grunde argumentieren wir, dass sich an der Methode der ereignisdiskreten Simulation im Betrachtungszeitraum - also in den letzten 36 Jahren - nichts Wesentliches geändert hat. Dies ist nicht als Kritik an der ereignisdiskreten Simulation zu werten, sondern könnte heute als Resilienz bezeichnet werden. Die ereignisdiskrete Simulation ist weiterhin hochrelevant und zeigt im Zusammenspiel mit informationstechnischen Neuerungen ihr nach wie vor großes Nutzenpotential.

Hafner, Anke;
Mobile Assistenzsysteme in der Intralogistikplanung der Automobilindustrie - Gestaltung, Nutzen und Akzeptanz Augmented Reality-basierter Mensch-Maschine-Schnittstellen. - Ilmenau : Universitätsverlag Ilmenau, 2022. - 1 Online-Ressource (XVII, 245 Seiten, Seite XIX-LXVI)
Technische Universität Ilmenau, Dissertation 2022

Steigende Herausforderungen, wie z.B. ein intensiver Wettbewerbsdruck hinsichtlich Innovationen und eine damit einhergehende Individualisierung der Produkte sowie eine steigende Internationalisierung führen zu einem Wandel in der Automobilbranche. Die Kombination der Faktoren führt zu einem Anstieg der Komplexität in der Fahrzeugherstellung sowie einem Anstieg der Komplexität des Produktionssystems. Eine wichtige Rolle in der Automobilindustrie wird u. a der Intralogistik zugeschrieben. Die Intralogistik, ausgehend von der Komplexität der Automobilindustrie, steht ebenso gewissen Herausforderungen gegenüber. Um der steigenden Komplexität entgegen zu wirken, besteht der Bedarf an innovativen Lösungen seitens der Logistikplanung. Eine Möglichkeit in der Intralogistikplanung kann der Einsatz von innovativen Mensch-Maschine Schnittstellen in Form von mobilen Assistenzsystemen mit Augmented Reality sein. Der Technologie Augmented Reality werden zahlreiche positive Eigenschaften zugesagt. Im Bereich der Intralogistik, bezogen auf Augmented Reality, wird vermehrt die operative Intralogistik, wie z. B. die Kommissionierung und das Picken, betrachtet. Obwohl auf dem Markt zahlreiche Technologien bestehen und diese in verschiedenen Anwendungsbereichen eingesetzt werden, existieren in der Intralogistikplanung keine flächendeckenden Anwendungen im Sinne eines mobilen Assistenzsystems mit Augmented Reality in einer Produktionshalle. Ausgehend davon wird in diesem Buch ein Use Case und darauf basierende Prototypen für eine Augmented Reality -basierte Intralogistikplanung in der Automobilindustrie entwickelt. Dabei liegt der Fokus auf der Schaffung eines mobilen Assistenzsystems, welches eine durchgängige Augmented Reality -basierte Intralogistikplanung in der Endmontage der Automobilindustrie unterstützt. Für die Ausgestaltung des Use Cases erfolgt die Durchführung einer systematischen Literaturanalyse und das Analysieren von vorhandenen intralogistischen Planungsprozessen mit dem Ziel, die relevanten Planungsprozesse für den Einsatz des mobilen Assistenzsystems zu definieren. Auf Basis von funktionalen und nicht-funktionalen Anforderungen werden zwei Prototypen implementiert. Die prototypische Umsetzung erfolgt sowohl nach einem SLAM-basierten als auch nach einem hybriden Trackingansatz. Ferner werden die Prototypen anhand eines Feldexperiments validiert. Darüber hinaus wird für die Evaluation der Akzeptanz von Augmented Reality in der Intralogistikplanung das Technology Acceptance Model empirisch ausgewertet.
Wörrlein, Benjamin; Straßburger, Steffen
Hochaufgelöste Energieprofile durch hybride Simulation. - In: ASIM SST 2022 Proceedings Langbeiträge, (2022), S. 243-251

The price of a commodity, as electricity, is determined on a commodity market. A market is efficient when the supply and demand in the market are at an equilibrium. Efficient markets run on information. Information can cause a spontaneous and instantaneous change within the supply and demand in a market. The market communicates this new equilibrium through the change of the price of a commodity. In the electricity market the supplier and consumer communicate through electrical load profiles. A load profile signals when and how much energy should be consumed within a certain time frame without causing a change in the price of electricity. Creating such load profiles is commonly done by the supplier of energy by means of standard load profiles. Here we propose a data-driven simulation-based method that allows for the consumer to create its own specific load profile, which potentially will bring down the cost of energy consumed.