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: 45
Created on: Sat, 03 Dec 2022 23:17:46 +0100 in 0.0447 sec


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.



https://dx.doi.org/10.11128/sne.32.tn.10611
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen
Simulationsbasiertes Deep Reinforcement Learning für Modulare Produktionssysteme. - In: ASIM SST 2022 Proceedings Langbeiträge, (2022), S. 65-72

Modulare Produktionssysteme sollen die traditionelle Fließbandproduktion in der Automobilindustrie ablösen. Die Idee dabei ist, dass sich hochgradig individualisierte Produkte dynamisch und autonom durch ein System flexibler Arbeitsstationen bewegen können. Dieser Ansatz stellt hohe Anforderungen an die Planung und Organisation solcher Systeme. Da jedes Produkt seinen Weg durch das System frei und individuell bestimmen kann, kann die Implementierung von Regeln und Heuristiken, die die Flexibilität des Systems zur Leistungssteigerung ausnutzen, in diesem dynamischen Umfeld schwierig sein. Transportaufgaben werden in der Regel von fahrerlosen Transportsystemen (FTS) ausgeführt. Daher bietet die Integration von KI-basierten Steuerungslogiken eine vielversprechende Alternative zu manuell implementierten Entscheidungsregeln für den Betrieb der FTS. In diesem Beitrag wird ein Ansatz für den Einsatz von Reinforcement Learning (RL) in Kombination mit Simulation vorgestellt, um FTS in modularen Produktionssystemen zu steuern. Darüber hinaus werden Untersuchungen zu dessen Flexibilität und Skalierbarkeit durchgeführt.



https://dx.doi.org/10.11128/arep.20.a2007
Bergmann, Sören;
Optimierung des Designs modularer Montagesysteme. - In: ASIM SST 2022 Proceedings Langbeiträge, (2022), S. 15-22

Der Wunsch nach mehr Flexibilität in Fertigungssystemen, insbesondere, wenn verschiedene Produkte bzw. viele Produktvarianten in einem Produktionssystem gefertigt werden, führt, besonders in der Automobilindustrie, zur Abkehr vom Fertigungsprinzip der klassischen Linienfertigungen hin zu eher flexiblen und werkstattorientierten Produktionssystemen. Eine der Herausforderungen in diesen so genannten modularen Montage- bzw. Produktionssystemen ist das Systemdesign, insbesondere die Zuordnung der Tätigkeiten auf die einzelnen Fertigungsinseln. Ein Ansatz, diese Zuordnung zu verbessern bietet die simulationsbasierte Optimierung. In diesem Beitrag wird ein Konzept zur simulationsbasierten Optimierung des Designs modularer Montagesysteme vorgestellt und anhand einer Fallstudie demonstriert. Zum Einsatz kommen hierbei genetische Algorithmen, speziell der NSGA-II-Algorithmus, welcher auch mehrkriterielle Optimierung ermöglicht.



https://dx.doi.org/10.11128/arep.20.a2006
Genath, Jonas; Bergmann, Sören; Straßburger, Steffen; Spieckermann, Sven; Stauber, Stephan
Data farming and knowledge discovery in simulation data : development of an integrated solution
Data Farming und Wissensentdeckung in Simulationsdaten : Entwicklung einer integrierten Lösung. - In: Zeitschrift für wirtschaftlichen Fabrikbetrieb, ISSN 2511-0896, Bd. 117 (2022), 3, S. 144-150

Simulation als Methode der Digitalen Fabrik hat sich seit langem zur Unterstützung der Planung von Produktions- und Logistiksystemen etabliert. In Ergänzung zu bisher vorherrschenden Simulationsstudien wird bei der hier vorgestellten Methode der Wissensentdeckung in Simulationsdaten ein Simulationsmodell als Datengenerator verwendet. Dadurch können mittels Data-Mining- und Visual-Analytics-Methoden versteckte und potenziell nützliche Ursache-Wirkungs-Beziehungen in den generierten Daten aufgedeckt werden. Bislang fehlte es jedoch an integrierten Softwarelösungen für die Praxis.



https://doi.org/10.1515/zwf-2022-1032
Feldkamp, Niclas; Bergmann, Sören; Conrad, Florian; Straßburger, Steffen
A method using generative adversarial networks for robustness optimization. - In: ACM transactions on modeling and computer simulation, ISSN 1558-1195, Bd. 32 (2022), 2, S. 12:1-12:22

The evaluation of robustness is an important goal within simulation-based analysis, especially in production and logistics systems. Robustness refers to setting controllable factors of a system in such a way that variance in the uncontrollable factors (noise) has minimal effect on a given output. In this paper, we present an approach for optimizing robustness based on deep generative models, a special method of deep learning. We propose a method consisting of two Generative Adversarial Networks (GANs) to generate optimized experiment plans for the decision factors and the noise factors in a competitive, turn-based game. In a case study, the proposed method is tested and compared to traditional methods for robustness analysis including Taguchi method and Response Surface Method.



https://doi.org/10.1145/3503511
Genath, Jonas; Bergmann, Sören; Feldkamp, Niclas; Straßburger, Steffen
Automation within the process of knowledge discovery in simulation data : characterization of the result data
Automatisierung im Prozess der Wissensentdeckung in Simulationsdaten : Charakterisierung der Ergebnisdaten. - In: Simulation in Produktion und Logistik 2021, (2021), S. 367-376
Literaturangaben

The traditional application of simulation in production and logistics is usually aimed at changing certain parameters in order to answer clearly defined objectives or questions. In contrast to this approach, the method of knowledge discovery in simulation data (KDS) uses a simulation model as a data generator (data farming). Subsequently using data mining methods, hidden, previously unknown and potentially useful cause-effect relationships can be uncovered. So far, however, there is a lack of guidelines and automatization-tools for non-experts or novices in KDS, which leads to a more difficult use in industrial applications and prevents a broader utilization. This paper presents a concept for automating the first step of the KDS, which is the process of characterization of the result data, using meta learning and validates it on small case study.



Genath, Jonas; Bergmann, Sören; Spieckermann, Sven; Stauber, Stephan; Feldkamp, Niclas
Development of an integrated solution for data farming and knowledge discovery in simulation data :
Entwicklung einer integrierten Lösung für das Data Farming und die Wissensentdeckung in Simulationsdaten. - In: Simulation in Produktion und Logistik 2021, (2021), S. 377-386
Literaturangaben

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 is 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 for 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.



Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen
Simulation-based deep reinforcement learning for modular production systems. - In: 2020 Winter Simulation Conference (WSC), (2020), S. 1596-1607

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. Since each product can define its way through the system freely and individually, implementing rules and heuristics that leverage the flexibility in the system in order to increase performance can be difficult in this dynamic environment. Transport tasks are usually carried out by automated guided vehicles (AGVs). Therefore, integration of AI-based control logics offer a promising alternative to manually implemented decision rules for operating the AGVs. This paper presents an approach for using reinforcement learning (RL) in combination with simulation in order to control AGVs in modular production systems. We present a case study and compare our approach to heuristic rules.



https://doi.org/10.1109/WSC48552.2020.9384089
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen
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
Bergmann, Sören; Feldkamp, Niclas; Conrad, Florian; Straßburger, Steffen
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