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: Thu, 28 Mar 2024 23:14:17 +0100 in 0.0674 sec


Bergmann, Sören; Ehrle, Steven
Basic layouts for modular assembly systems - a simulation-based comparison :
Grundlayouts für modulare Montagesysteme - ein simulationsbasierter Vergleich. - In: Simulation in Produktion und Logistik 2023, (2023), S. 197-206

The article discusses the challenges posed by increased individualization of products, shorter product life cycles, and external factors on the flexibility of modern production systems. In particular, flexible workshop-oriented manufacturing principles are being implemented to replace or supplement traditional assembly lines, with various terms such as "modular assembly" and "matrix production" etc. used to describe similar concepts. The article presents these concepts under the umbrella term of modular production or assembly systems, which utilize adaptable workstations and autonomous vehicles to transport production orders between stations. The design of such systems is crucial to their performance, with considerations such as task allocation, material supply, and fleet sizing requiring complex interplay. The article compares traditional matrix layouts with alternative options, such as single-lane pathways and non-matrix layouts like honeycomb or star shapes, using simulationbased analysis to evaluate their potential impact on system performance.



https://doi.org/10.22032/dbt.57809
Bergmann, Sören; Feldkamp, Niclas; Souren, Rainer; Straßburger, Steffen
Simulation in Produktion und Logistik 2023 : ASIM Fachtagung : 20. Fachtagung, 13.-15. September 2023, TU Ilmenau. - Ilmenau : Universitätsverlag Ilmenau, 2023. - 1 Online-Ressource (XII, 485 Seiten). - (ASIM-Mitteilung ; Nr. 187)
https://doi.org/10.22032/dbt.57476
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.



https://doi.org/10.1109/WSC57314.2022.10015350
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.