Publikationen

In the context of online- and symbiotic simulation, the precise initialization of simulation models based on the state of the physical system is a fundamental requirement. In these simulations, the simulation model typically serves as an operational decision support tool. Obviously, it can therefore not start empty and idle. The accurate capturing of initial conditions is fundamental for the quality of the model based predictions. In literature, it is only generally stated that the simulation model must maintain a close connection with the physical system. Our work systematically investigates which data from the physical system is needed for initialization, how it shall be transferred into the simulation model in a standardized way, and which potential problems must be solved in the simulation system to adequately initialize its model elements. We present a solution based on the core manufacturing simulation data (CMSD) standard, suggest necessary extensions and demonstrate a prototypical implementation.

Simulation hat sich in vielen Anwendungsbereichen innerhalb produzierender Unternehmen zu einem nicht mehr weg zu denkenden Werkzeug zur Entscheidungsfindung bzw. -unterstützung für nachhaltiges Wirtschaften entwickelt. Allerdings führt die Erstellung und Integration von Simulationsmodellen sowie deren Nutzung immer noch zu erheblichen Kosten für Unternehmen. Seit geraumer Zeit werden Ansätze verfolgt, die ermöglichen sollen, Modelle effizienter zu erstellen und zu nutzen, aber erst die konsequente Nutzung eines Standards zur Datenmodellierung und zum Datenaustausch ermöglicht eine breitere Nutzung solcher Ansätze. In dieser Arbeit wird gezeigt, dass CMSD ein geeigneter Standard sowohl für die Erstellung von Simulationsmodellen als auch für deren Initialisierung ist.

Automatic generation of simulation models has been a recurring topic in scientific papers for years. A common problem of all known model generation approaches is the generation of dynamic behavior, e.g. buffering or control strategies. This paper introduces a novel methodology for generation of dynamic behavior, based on artificial neural networks, which is usable directly in the simulation. We also test the approach in a manageable scenario; all results are illustrated via small simulation experiments.

Today, due to exciting developments in advanced computing techniques and technologies, many scientists can now make use of dedicated high speed networks and high performance computing. This so-called "e-science" is enabling scientists across many fields to work together in global virtual research communities. What do these advancements mean for modeling and simulation? This advanced tutorial investigates two key areas that are affecting the way M&S projects are being developed and deployed. Grid Computing addresses the use of many computers to speed up applications. Simulation Interoperability deals with linking together remote simulations and/or speeding up the execution of a single run. Through the use of case studies we hope to show that both these areas are making a major impact on the practice of M&S in both industry and science, as well as in turn supporting the future capabilities of e-Science.