A Novel Method for General Hierarchical System Modeling via Colored Petri Nets Based on Transition Extractions from Real Datasets

The Colored Petri net (CPN) has evolved into a complete modeling language, which is based on the object-oriented paradigm. Although this language is quite complete and can be used to accurately model a wide variety of systems, it seems that there is a lack of certain modeling rules, which can be used to generate models based on real datasets. This paper is a first attempt to incorporate sequences of real system events and translate them into sequences of Petri net transitions. Then, well-defined modeling rules control the model generation based on these transitions. The specific entities that take part in each of the real system events, as well as their attributes are also considered. The models produced using real data are structured because, in the majority of real systems, each incurring event usually affects a part of the system. Then, this effect is propagated to the entire system. Therefore, it is much easier to divide the entire model into distinct parts when real data are used and then interconnect these parts to build an entire model. This is a very important aspect when modeling very large systems. To test our approach, we used the real data from a courier company to generate its model. Our simulation results have shown that we managed to obtain quite accurate results through the model produced by the actual data.