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 0.0675 sec


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
Bergmann, Sören; Straßburger, Steffen
Automatische Modellgenerierung - Stand, Klassifizierung und ein Anwendungsbeispiel. - In: Ablaufsimulation in der Automobilindustrie, (2020), S. 333-347

Die automatische Modellgenerierung (AMG) ist ein Ansatz, der darauf abzielt, sowohl die Aufwände einer Simulationsstudie zu senken als auch die Qualität der erzeugten Modelle zu verbessern. Unter automatischer Modellgenerierung werden im Kontext der Simulation verschiedene Ansätze subsumiert, die es erlauben, Simulationsmodelle oder zumindest Teile von Simulationsmodellen mittels Algorithmen zu erzeugen. Eine umfassende Klassifizierung der Ansätze nach verschiedenen Merkmalen ist Ausgangspunkt weiterer Betrachtungen des Beitrags, in denen u. a. verschiedene technische Ansätze zur Modellgenerierung diskutiert werden. Weiterhin werden ergänzende Techniken, die die eigentliche Modellgenerierung flankierenden, wie z. B. die automatische Modellinitialisierung, diskutiert. Als ein möglicher Lösungsansatz wird beispielhaft ein Framework zur automatischen Modellgenerierung, -initialisierung und -adaption, welches das standardisierte Core Manufacturing Simulation Data (CMSD) Format als Basis nutzt, beschrieben.



https://doi.org/10.1007/978-3-662-59388-2_23
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen; Schulze, Thomas
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
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen
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.



Wörrlein, Benjamin; Bergmann, Sören; Feldkamp, Niclas; Straßburger, Steffen
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.



Bergmann, Sören; Feldkamp, Niclas; Straßburger, Steffen
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
Schulte, Julian; Feldkamp, Niclas; Bergmann, Sören; Nissen, Volker
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
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen; Borsch, Erik; Richter, Magnus; Souren, Rainer
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