Consideration about the possibility to integrate vague uncertainty notions into numerical simulation modeling tools may be a very interesting research field. In this way, it will be possible to exploit more efficient and robust modeling evaluation tools in the study of high productivity and flexibility production systems. In literature, few works investigated on the possibility to cope with the lack of numerical models able to deal with ill-defined uncertainty. In particular, if it is possible to describe uncertainty by statistical distribution, the methods of classical discrete event simulation theory are able to model the considered system thoroughly and may be regarded as an exhaustive tool. Otherwise, if uncertainty cannot be described by statistical distribution, no robust methods tools are available to model and analyze discrete complex dynamic systems. In this work, the integration of Fuzzy Sets in discrete event simulation theory is analyzed. Firstly, uncertainty is considered from different point of views and all the necessary issues to introduce fuzziness in discrete event simulation models are illustrated. Then, a possibility-based approach is considered and fuzzy set theory concepts have been introduced in such a context. The soundness of simulation mechanisms has been formally established by considering the new questions arising from the description of system variables as fuzzy sets. Finally, the application of the proposed methodology to a simplified test case is showed and the obtained results are presented.