TU Ilmenau

Dr. Niclas Feldkamp

Room
Werner Bischoff Building
Room F1110

niclas.feldkamp@tu-ilmenau.de

+49 (0) 3677 69-4044

 

Office hours

Consultation hours are only available by prior individual arrangement.

Entries in citation databases

Research Gate

ACM Digital Library

List of publications

Results: 35
Created on: Thu, 28 Mar 2024 23:14:27 +0100 in 0.0619 sec


Schulte, Julian; Feldkamp, Niclas; Bergmann, Sören; Nissen, Volker
Bilevel innovization: knowledge discovery in scheduling systems using evolutionary bilevel optimization and visual analytics. - In: GECCO'18 companion, ISBN 978-1-4503-5764-7, (2018), S. 197-198

https://doi.org/10.1145/3205651.3205726
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen
Online analysis of simulation data with stream-based data mining. - In: SIGSIM-PADS'17, (2017), S. 241-248

https://doi.org/10.1145/3064911.3064915
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen; Schulze, Thomas; Akondi, Praneeth; Lemessi, Marco
Knowledge discovery in simulation data - a case study for a backhoe assembly line. - In: WSC'17, ISBN 978-1-5386-3428-8, (2017), S. 4456-4458

Discrete event simulation is an established and popular technology for investigating the dynamic behavior of complex manufacturing and logistics systems. Besides conventional simulation studies that focus on single model aspects answering project specific analysis questions, new methods of broad scale experiment design and system analysis emerge alongside new developments of computational power and data processing. This enables to investigate the bandwidth of possible system behavior in a more in-depth way. In this work we applied our previously developed methodology of knowledge discovery in simulation data onto an industrial case study for a backhoe loader manufacturing facility.



https://doi.org/10.1109/WSC.2017.8248162
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen; Schulze, Thomas
Knowledge discovery and robustness analysis in manufacturing simulations. - In: WSC'17, ISBN 978-1-5386-3428-8, (2017), S. 3952-3963

Discrete event simulation is an established methodology for investigating the dynamic behavior of complex manufacturing and logistics systems. Traditionally, simulation experts conduct experiments for predetermined system specifications focusing on single model aspects and specific analysis questions. In addition to that, the concept of data farming and knowledge discovery is an ongoing research issue that consists of broad scale experimentation and data mining assisted analysis of massive simulation output data. As an extension to this approach, we propose a concept for investigating the robustness of complex manufacturing and logistic systems which are often very sensitive to variation and noise. Based on Taguchis loss function, we developed a concept including data farming and visual analytics methodologies to investigate sources of variation in a model and the factor values that make a configuration robust. The concept is demonstrated on an exemplary case study model.



https://doi.org/10.1109/WSC.2017.8248105
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen; Schulze, Thomas
Data farming for production and logistics :
Data Farming im Kontext von Produktion und Logistik. - In: Simulation in Produktion und Logistik 2017, ISBN 978-3-7376-0192-4, (2017), S. 169-178

Simulation is an established methodology for planning and evaluating manufacturing and logistics systems. Usually simulations experts conduct experiments for a prior defined goal and by minimizing the number of simulation runs. In contrast to that, data farming describes an approach for using the simulation model as a data generator for broad scale experimentation with a broader coverage of system behaviour. This paper demonstrates how to apply data farming methodologies on simulation models in the context of production and logistics and how to analyse massive amounts of simulation data using data mining and visual analytics.



Bergmann, Sören; Feldkamp, Niclas; Straßburger, Steffen
Emulation of control strategies through machine learning in manufacturing simulations. - In: Journal of simulation, ISSN 1747-7786, Bd. 11 (2017), 1, S. 38-50

Discrete-event simulation is a well-accepted method for planning, evaluating, and monitoring processes in production and logistics. To reduce time and effort spent on creating simulation models, 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 dynamic behavior of the modeled 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 this paper, we summarize our work investigating the suitability of various data mining and supervised machine learning methods for emulating job scheduling decisions based on data obtained from production data acquisition. We report on the performance of the algorithms and give recommendations for their application, including suggestions for their integration in simulation systems.



http://dx.doi.org/10.1057/s41273-016-0006-0
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen; Schulze, Thomas
Knowledge discovery in simulation data: a case study of a gold mining facility. - In: Simulating complex service systems, ISBN 978-1-5090-4486-3, (2016), S. 1607-1618

Discrete event simulation is an established methodology for investigating the dynamic behavior of complex systems. Apart from conventional simulation studies, which focus on single model aspects and answering specific analysis questions, new methods of broad scale experiment design and analysis emerge in alignment with new possibilities of computation and data processing. This paper outlines a visually aided process for knowledge discovery in simulation data which is applied onto a real world case study of a mining facility in Western Australia.



https://doi.org/10.1109/WSC.2016.7822210
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen
Innovative Analyse- und Visualisierungsmethoden für Simulationsdaten. - In: , (2016), S. 1737-1748

https://nbn-resolving.org/urn:nbn:de:gbv:ilm1-2016100035
Bergmann, Sören; Feldkamp, Niclas; Straßburger, Steffen
Gestaltungsmöglichkeiten selbst-adaptierender Simulationsmodelle. - In: , (2016), S. 1713-1724

https://nbn-resolving.org/urn:nbn:de:gbv:ilm1-2016100035
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