TU Ilmenau

Dr. Sören Bergmann

Room

Werner Bischoff Building

Room F1110

soeren.bergmann@tu-ilmenau.de

+49 (0) 3677 69-4045

 

Consultation hours

Consultation hours are only available by prior individual arrangement.

Research focus

  • Automatic generation and adaptation of simulation models
  • Data mining, visual analytics for simulation data analysis
  • Use of AI methods in the context of hybrid simulation
  • Verification and validation of simulation models
  • Integration of simulation into operational IT infrastructures
  • Standards in the context of simulation, especially CMSD

Professional experience

  • 2004-2007 Software Developer/ Business Consultant (BonkConsulting GmbH)
  • Oct 2007-Aug 2018 Research assistant in the FG Business Informatics for Industrial Companies
  • 09/2012 PhD with distinction
  • since Aug 2018 Research Assistant in the FG Information Technology in Production and Logistics

Memberships

  • Working Group Simulation (ASIM) of the German Informatics Society (GI)

List of publications (only works according to the university bibliography of the TU Ilmenau)

Results: 48
Created on: Wed, 27 Mar 2024 23:36:36 +0100 in 0.0658 sec


Bergmann, Sören; Feldkamp, Niclas; Straßburger, Steffen
Approximation of dispatching rules for manufacturing simulation using data mining methods. - In: Proceedings of the 2015 Winter Simulation Conference, ISBN 978-1-4673-9743-8, (2015), S. 2329-2340

Discrete-event simulation is a well-accepted method for planning, evaluating, and monitoring processes in production and logistics contexts. In order to reduce time and effort spent on creating the simulation model, automatic simulation model generation is an important area in modeling methodology research. When automatically generating a simulation model from existing data sources, the correct reproduction of the dynamic behavior of the modelled system is a common challenge. One example is the representation of dispatching and scheduling strategies of production jobs. When generating a model automatically, the underlying rules for these strategies are typically unknown but yet have to be adequately emulated. In previous work, we presented an approach to approximate the behavior through artificial neural networks. In this paper, we investigate the suitability of various other data mining and supervised machine learning methods for emulating job scheduling decisions with data obtained from production data acquisition.



http://dx.doi.org/10.1109/WSC.2015.7408344
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen
Visual analytics of manufacturing simulation data. - In: Proceedings of the 2015 Winter Simulation Conference, ISBN 978-1-4673-9743-8, (2015), S. 779-790

Discrete event simulation is an accepted technology for investigating the dynamic behavior of complex manufacturing systems. Visualizations created within simulation studies often focus on the animation of the dynamic processes of a single simulation run, supplemented with graphs of certain performance indicators obtained from replications of a simulation run or a few manually conducted simulation experiments. This paper suggests a much broader visually aided analysis of simulation input and output data and their relations than it is commonly applied today. Inspired from the idea of visual analytics, we suggest the application of data farming approaches for obtaining datasets of a much broader spectrum of combinations of input and output data. These datasets are then processed by data mining methods and visually analyzed by the simulation experts. This process can uncover causal relationships in the model behavior that were previously not known, leading to a better understanding of the systems behavior.



http://dx.doi.org/10.1109/WSC.2015.7408215
Bergmann, Sören; Straßburger, Steffen
On the use of the Core Manufacturing Simulation Data (CMSD) standard: experiences and recommendations. - Ilmenau : Univ.-Bibliothek. - Online-Ressource (PDF-Datei: 11 S.)Druck-Ausgabe: Fall Simulation Interoperability Workshop (2015 Fall SIW) : Orlando, Florida, USA, 31 August-4 September 2015 / SISO - Simulation Interoperability Standards Organization. - Red Hook, NY : Curran Associates, Inc., 2016. - Seite 119-129

The Core Manufacturing Simulation Data (CMSD) information model is defined by SISO standards SISO-STD-008-01-2012 and SISO-STD-008-2010. The main objective of CMSD is to facilitate interoperability between simulation systems and other information systems in the manufacturing domain. While CMSD is mainly intended as standardized data exchange format, its capabilities go beyond simple data exchange. Frequently CMSD based system descriptions are used for purposes of automatic simulation model generation. In this paper, we report on practical experiences using the CMSD standard for such purposes as well as for purposes of simulation model initialization and simulation output data collection. Based on our experiences we suggest potential enhancements for a future revision of the standard.



http://www.db-thueringen.de/servlets/DocumentServlet?id=26816
Bergmann, Sören; Feldkamp, Niclas; Hinze, Ulrich; Straßburger, Steffen
Emulation of control strategies through machine learning in manufacturing simulations :
Abbildung von Steuerungslogiken durch maschinelles Lernen für die Simulation von Produktionssystemen. - In: Simulation in production and logistics 2015, (2015), S. 481-490

In the context of discrete-event simulation of production and logistics systems, modelling an exact representation of the real system is needed for obtaining sound and reliable results. The automatic generation of simulation models is an approach for saving time and effort for creating models and, therefore, it is a recurring issue in modelling methodology research. In automatic model generation, the approximation of dynamic behaviour is a challenging problem. This is for example the case when the dispatching and scheduling of production jobs needs to be adequately emulated, but the underlying rules are unknown. In previous work, we presented an approach for approximating dynamic behaviour through artificial neural networks. In this paper, we propose an improved approach and investigate its suitability again with artificial neuronal networks as well as with other data mining and supervised machine learning methods.



Feldkamp, Niclas; Bergmann, Sören; Bergmann, Sören *1979-*; Straßburger, Steffen;
Knowledge discovery in manufacturing simulations. - In: SIGSIM PADS'15, ISBN 978-1-4503-3565-2, (2015), S. 3-12

Discrete event simulation studies in a manufacturing context are a powerful instrument when modeling and evaluating processes of various industries. Usually simulation experts conduct simulation experiments for a predetermined system specification by manually varying parameters through educated assumptions and according to a prior defined goal. Moreover, simulation experts try to reduce complexity and number of simulation runs by excluding parameters that they consider as not influential regarding the simulation project scope. On the other hand, today's world of big data technology enables us to handle huge amounts of data. We therefore investigate the potential benefits of designing large scale experiments with a much broader coverage of possible system behavior. In this paper, we propose an approach for applying data mining methods on simulation data in combination with suitable visualization methods in order to uncover relationships in model behavior to discover knowledge that otherwise would have remained hidden. For a prototypical demonstration we used a clustering algorithm to divide large amounts of simulation output datasets into groups of similar performance values and depict those groups through visualizations to conduct a visual investigation process of the simulation data.



Bergmann, Sören; Parzefall, Florian; Straßburger, Steffen
Webbasierte Animation von Simulationsläufen auf Basis des Core Manufacturing Simulation Data (CMSD) Standards. - In: ASIM 2014, 22. Symposium Simulationstechnik, 3. bis 5. September 2014, HTW Berlin; Tagungsband, (2014), S. 63-70

Animation von Simulationsläufen ist für viele Anwendungen ein nicht zu unterschätzendes Hilfsmittel. Die Nutzungsmöglichkeiten sind hierbei mannigfaltig, sie reichen von der Validierung der Modelle bis hin zur Ergebnispräsentation von Simulationsstudien. Dem Nutzen steht mitunter aber auch ein nicht zu unterschätzender Aufwand gegenüber, gerade im Kontext der automatischen webbasierten Simulation sind oft geeignete Animationen nicht verfügbar. Im Rahmen dieses Papers wird ein Ansatz vorgestellt, welcher ein bestehendes Framework zur automatischen Modellgenerierung, -initialisierung und Simulationsdurchführung inklusive Ergebnisauswertung auf Basis des Core Manufacturing Simulation Data (CMSD) Standards um die Möglichkeit der vollständig automatischen webbasierten Animation erweitert. Hierzu wird neben der Diskussion der Grundlagen der Animation das bestehende Framework und der dem Framework zugrunde liegende CMSD-Standard vorgestellt. Des Weiteren werden verschiedene Implementierungstechnologien vom Streamen von Videos über das Nutzen von Plug-Ins wie Flash oder Java Applets bis hin zu modernen Techniken wie HTML 5, CSS3 und JavaScript kritisch beleuchtet. Abschließend wird eine prototypische Implementierung mittels HTML 5 Canvas und den JavaScript Frameworks JQuery und KineticJS vorgestellt.



Bergmann, Sören; Stelzer, Sören; Straßburger, Steffen
On the use of artificial neural networks in simulation-based manufacturing control. - In: Journal of simulation, ISSN 1747-7786, Bd. 8 (2014), 1, S. 76-90

The automatic generation of simulation models has been a recurring research topic for several years. In manufacturing industries, it is currently also becoming a topic of high practical relevance. A well-known challenge in most model generation approaches is the correct reproduction of the dynamic behaviour of model elements, for example, buffering or control strategies. This problem is especially relevant in simulation-based manufacturing control. In such scenarios, simulation models need to reflect the current state and behaviour of the real system in a highly accurate way, otherwise its suggested control decisions may be inaccurate or even dangerous towards production goals. This paper introduces a novel methodology for approximating dynamic behaviour using artificial neural networks, rather than trying to determine exact representations. We suggest using neural networks in conjunction with traditional material flow simulation systems whenever a certain decision cannot be made ex ante in the model generation process due to insufficient knowledge about the behaviour of the real system. In such cases the decision is delegated to the neural network, which is connected to the simulation system at runtime. Training of the neural network is performed by observation of the real systems decision and based on the evaluation of data that can be gained through production data acquisition. Our approach has certain advantages compared to other approaches and is especially well suited in the context of on-line simulation and simulation-based operational decision support. We demonstrate the applicability of our methodology using a case study and report on performance results.



http://dx.doi.org/10.1057/jos.2013.6
Bergmann, Sören;
Automatische Generierung adaptiver Modelle zur Simulation von Produktionssystemen. - Ilmenau : Universitätsverlag Ilmenau, 2013. - Online-Ressource : Ilmenau, Techn. Univ., Diss., 2013
Parallel als Druckausg. erschienen

Die Simulation von Produktionsprozessen wird heute in einer Vielzahl von Branchen eingesetzt. Simulation dient hierbei zur Analyse, dem Design und der Optimierung der Produktions- und Logistikprozesse und dem dabei anfallenden Ressourceneinsatz und kann hierbei sowohl in der Planung, Inbetriebnahme als auch während des operativen Betriebs genutzt werden. Den unbestritten großen Potentialen der Materialflusssimulation in Unternehmen stehen entsprechend hohe Aufwände entgegen. Diese entstehen sowohl bei der Implementierung der Modelle als auch deren Nutzung. Durch schlechte Integration und Standardisierung der Simulation, steigende Anforderungen der Unternehmen bzgl. Genauigkeit, Flexibilität, Anpassbarkeit, Geschwindigkeit, Kosten, Wiederverwendbarkeit, Zyklen und phasenübergreifender Nutzbarkeit usw. werden die Aufwände teils unnötig gesteigert. Ein Ansatz, der seit einigen Jahren immer wieder als ein Lösungsbeitrag für eine bessere Nutzung der Simulation auch gerade in KMU's betrachtet wird, ist die automatische Generierung von Simulationsmodellen. Unter automatischer Modellgenerierung werden verschiedene Ansätze subsumiert, die erlauben Simulationsmodelle oder zumindest Teile von Modellen mittels Algorithmen zu erzeugen. Bisher wurde kein Ansatz veröffentlicht, der für einen breiteren Nutzerkreis und über einen speziellen Teilbereich hinaus gute Ergebnisse liefert.In dieser Arbeit wurde ein umfassendes Rahmenwerk zur Integration bzw. Automatisierung der Simulation entworfen und validiert. Es wurden sowohl organisatorische, methodische als auch prototypisch technische Komponenten betrachtet. In diesem Zusammenhang wird die These vertreten, dass eine breit anwendbare automatische Modellgenerierung allein durch die Nutzung von Standards zum Datenaustausch bzw. zur Konzeptmodellerstellung sinnvoll zu implementieren ist. Konkret wurde der Core Manufacturing Simulation Data (CMSD) Standard genutzt bzw. bildet die Referenzanwendung des Standards die Basis der gesamten Arbeit. Die Unterstützung aller Simulationsphasen, d.h. nicht allein der Modellerstellung sondern auch der Alternativenbildung, Initialisierung, Ergebnisauswertung usw. in allen Komponenten wird durchgehend gewährleistet. Weiterhin wurden konkret Modellgenerierungsmethoden und Verfahren zur Abbildung des dynamischen Verhaltens in Modellen klassifiziert und einzelne Lösungsansätze vorgestellt.



http://www.db-thueringen.de/servlets/DocumentServlet?id=23106
Bergmann, Sören; Stelzer, Sören; Straßburger, Steffen
A new web based method for distribution of simulation experiments based on the CMSD standard. - In: Proceedings of the 2012 Winter Simulation Conference (WSC), ISBN 978-1-4673-4779-2, (2012), insges. 12 S.

This article introduces a novel methodology for web based distribution of simulation experiments. The approach is related to themes such as web based applications, cloud computing or applications as a service, which have been recurring topics in scientific papers for years. The methodology is based on automatic model generation, initialization, and result analysis under usage of the CMSD standard. All user interactions are performed in web based user interfaces. Of special importance is that different simulations tools can be used in parallel without any additional effort. Furthermore the simulation tool actually used is transparent to the user. The applicability of our methodology is demonstrated for different production scenarios.



http://dx.doi.org/10.1109/WSC.2012.6464985
Bergmann, Sören; Stelzer, Sören; Wüstemann, Sascha; Straßburger, Steffen
Model generation in SLX using CMSD and XML stylesheet transformations. - In: Proceedings of the 2012 Winter Simulation Conference (WSC), ISBN 978-1-4673-4779-2, (2012), insges. 11 S.

This article introduces a novel methodology for automatic simulation model generation. The methodology is based on the usage of XML stylesheet transformations for generating the actual source code of the target simulation system. It is therefore especially well-suited for all language-based simulation systems. The prerequisite for using the methodology is an appropriate representation of the system under investigation in the Core Manufacturing Simulation Data (CSMD) format. The applicability of our methodology is demonstrated for the simulation language SLX as well as for the visualization system Proof Animation.



http://dx.doi.org/10.1109/WSC.2012.6464981