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.
Visual analytics of manufacturing simulation data. - In: Proceedings of the 2015 Winter Simulation Conference, ISBN 978-1-4673-9743-8, (2015), S. 779-790
Discrete event simulation is an accepted technology for investigating the dynamic behavior of complex manufacturing systems. Visualizations created within simulation studies often focus on the animation of the dynamic processes of a single simulation run, supplemented with graphs of certain performance indicators obtained from replications of a simulation run or a few manually conducted simulation experiments. This paper suggests a much broader visually aided analysis of simulation input and output data and their relations than it is commonly applied today. Inspired from the idea of visual analytics, we suggest the application of data farming approaches for obtaining datasets of a much broader spectrum of combinations of input and output data. These datasets are then processed by data mining methods and visually analyzed by the simulation experts. This process can uncover causal relationships in the model behavior that were previously not known, leading to a better understanding of the systems behavior.
Optimistic synchronization in the HLA 1516.1-2010: interoperably challenged. - Ilmenau : Univ.-Bibliothek. - Online-Ressource (PDF-Datei: 9 S.)Druck-Ausgabe: Fall Simulation Interoperability Workshop (2015 Fall SIW) : Orlando, Florida, USA, 31 August-4 September 2015 / SISO - Simulation Interoperability Standards Organization. - Red Hook, NY : Curran Associates, Inc., 2016. - Seite 167-175
Time Management can be considered as one of the key achievements of the High Level Architecture for Modeling and Simulation (HLA). While HLA's time management is supposed to offer a unique support for heterogeneous time advancement schemes, its practical use is often limited to conservative time advancement (e.g. using services such as nextMessageRequest/nextMessageRequestAvailable) or time stepped time advancement (e.g. using services such as timeAdvanceRequest/timeAdvanceRequestAvailable). In this paper, we investigate HLA's capabilities for supporting optimistic time advancement and the interoperability between optimistic and conservative federates. The results are strikingly disappointing. While HLA had initially taken off with the noble vision of federations including both optimistic and conservative federates within a single federation execution, the current implementations of two leading RTI vendors fall short of achieving this objective. Neither do they enable the efficient execution of federations consisting of purely optimistically synchronized federates nor do they facilitate interoperability between optimistic and conservative federates. This paper documents the observed problems and discusses potential limitations in the IEEE HLA 1516.1-2010 specification and its interpretation by RTI vendors.
On the use of the Core Manufacturing Simulation Data (CMSD) standard: experiences and recommendations. - Ilmenau : Univ.-Bibliothek. - Online-Ressource (PDF-Datei: 11 S.)Druck-Ausgabe: Fall Simulation Interoperability Workshop (2015 Fall SIW) : Orlando, Florida, USA, 31 August-4 September 2015 / SISO - Simulation Interoperability Standards Organization. - Red Hook, NY : Curran Associates, Inc., 2016. - Seite 119-129
The Core Manufacturing Simulation Data (CMSD) information model is defined by SISO standards SISO-STD-008-01-2012 and SISO-STD-008-2010. The main objective of CMSD is to facilitate interoperability between simulation systems and other information systems in the manufacturing domain. While CMSD is mainly intended as standardized data exchange format, its capabilities go beyond simple data exchange. Frequently CMSD based system descriptions are used for purposes of automatic simulation model generation. In this paper, we report on practical experiences using the CMSD standard for such purposes as well as for purposes of simulation model initialization and simulation output data collection. Based on our experiences we suggest potential enhancements for a future revision of the standard.
Emulation of control strategies through machine learning in manufacturing simulations :
Abbildung von Steuerungslogiken durch maschinelles Lernen für die Simulation von Produktionssystemen. - In: Simulation in production and logistics 2015, (2015), S. 481-490
In the context of discrete-event simulation of production and logistics systems, modelling an exact representation of the real system is needed for obtaining sound and reliable results. The automatic generation of simulation models is an approach for saving time and effort for creating models and, therefore, it is a recurring issue in modelling methodology research. In automatic model generation, the approximation of dynamic behaviour is a challenging problem. This is for example the case when the dispatching and scheduling of production jobs needs to be adequately emulated, but the underlying rules are unknown. In previous work, we presented an approach for approximating dynamic behaviour through artificial neural networks. In this paper, we propose an improved approach and investigate its suitability again with artificial neuronal networks as well as with other data mining and supervised machine learning methods.