Publications

In vielen produzierenden Unternehmen ist Energie ein wesentlicher Kostenfaktor. Energieaspekte werden deshalb in das Entscheidungssystem der Produktionsplanung und -steuerung einbezogen, um die Herstellungskosten zu senken. Die Simulation von Produktionsprozessen erfordert neben der Berücksichtigung technischer und logistischer Produktionsfaktoren auch die Integration von kontinuierlichen Energieverbräuchen. Da Fertigungssysteme im Allgemeinen in diskreten Simulationsmodellen beschrieben werden, könnte ein Ansatz, der die beiden Systemdynamiken kombiniert, vorteilhaft sein. Die kombinierte Simulation nutzt einen kontinuierlichen Simulationsansatz zur Abbildung des Energiebedarfs relevanter Produktionsprozesse und kombiniert diesen mit einem diskreten Simulationsansatz zur Abbildung von Material- und Logistikprozessen. Durch die Zusammenführung der Modelle können die Wechselwirkungen zwischen Materialfluss und Energieverbrauch in der Produktion realitätsnäher simuliert werden.

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.

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.

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.