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.
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.
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.
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.
A review of literature on simulation-based optimization of the energy efficiency in production. - In: Simulating complex service systems, ISBN 978-1-5090-4486-3, (2016), S. 1416-1427
Due to rising resource prices, the sustained use of energy has become a basic requirement for manufacturing companies to competitively perform on the market. Designing production processes therefore not only requires the consideration of logistical and technical production conditions but also the consistent optimization of resource consumption. As simulation technology has become a common tool for assessing dynamic production processes, the consideration of energy-related issues in this context is becoming a more frequent subject. The aim of this literature research is to summarize the current state-of-the-art in the field of energy management in production and its adjacent disciplines as well as to identify future research priorities for the simulation-based optimization of energy aspects. The accomplishment of this objective requires a methodological review focusing on the multidisciplinary combination of simulation technologies, including hybrid simulation, the integration of mathematical optimization approaches, and the domain-specific knowledge of energy-related subjects in production systems.
Innovative Analyse- und Visualisierungsmethoden für Simulationsdaten. - In: , (2016), S. 1737-1748
Gestaltungsmöglichkeiten selbst-adaptierender Simulationsmodelle. - In: , (2016), S. 1713-1724
Multikonferenz Wirtschaftsinformatik (MKWI) 2016 : Technische Universität Ilmenau, 09. - 11. März 2016. - Ilmenau : Universitätsverlag Ilmenau
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.
HLA-based optimistic synchronization with SLX. - In: Proceedings of the 2015 Winter Simulation Conference, ISBN 978-1-4673-9743-8, (2015), S. 1717-1728
The High Level Architecture for Modeling and Simulation (HLA) comes with the promise of facilitating interoperability between a wide variety of simulation systems. HLA's time management offers a unique support for heterogeneous time advancement schemes and differentiates HLA from other general interoperability standards. While it has been shown that HLA is applicable for connecting commercial off-the-shelf simulation packages (CSPs), the usage of HLA time management in this application area is virtually always limited to conservative synchronization. In this paper, we investigate HLA's capabilities concerning optimistic synchronization. For the first time, we show its use in combination with a commercial-off-the-shelf simulation package (CSP), namely the simulation system SLX. We report on implementation details, performance results, and potential limitations in the current HLA 1516.1-2010 standard and its interpretation by runtime infrastructure (RTI) software vendors.