The creation of large-scale simulation models is a difficult and time-consuming task. Yet simulation is one of the techniques most frequently used by practitioners in Operations Research and Industrial Engineering, as it is less limited by modeling assumptions than many analytical methods. The effective generation of simulation models is an important challenge. Due to the rapid increase in computing power, it is possible to simulate significantly larger systems than in the past. However, the verification and validation of these large-scale simulations is typically a very challenging task.
This thesis introduces a simulation framework that can generate a large variety of manufacturing simulation models. These models have to be described with a simulation data specification. This specification is then used to generate a simulation model which is described as a Petri net. This approach reduces the effort of model verification.
The proposed Petri net data structure has extensions for time and token priorities. Since it builds on existing theory for classical Petri nets, it is possible to make certain assertions about the behavior of the generated simulation model.
The elements of the proposed framework and the simulation execution mechanism are described in detail. Measures of complexity for simulation models that are built with the framework are also developed.
The applicability of the framework to real-world systems is demonstrated by means of a semiconductor manufacturing system simulation model.Ph.D.Committee Chair: Alexopoulos, Christos; Committee Co-Chair: McGinnis, Leon; Committee Member: Egerstedt, Magnus; Committee Member: Fujimoto, Richard; Committee Member: Goldsman, Davi
