Modeling and analysis of a flexible manufacturing system with petri net

In the competitive environment, which has become more difficult with the developments emerging day by day in the manufacturing sector, it has become necessary for enterprises to meet the ever-changing product demands and varieties in a timely manner. Companies that want to maintain their position in this competitive environment need to adapt their production systems to these conditions in order to adapt effectively to the specified market changes. This reveals the concept of "flexibility" in production systems. Due to the fact that today's dynamic and uncertain market conditions related to flexibility capabilities systems, "Flexible Manufacturing Systems" (FMS) standouts as a powerful competitive tool considering this challenging task. FMSs are systems that include automated systems with minimum personnel intervention, computer-aided machines, material-handling systems, robots, and conveyors that can be programmed automatically. Modeling and analysis of such complex systems require powerful tools. In this study, a flexible manufacturing cell (FMC) was modeled using the "Petri nets" (PN) tool, and performance analyzes were performed. Considered PN method; shows characteristics that are difficult to describe with traditional mathematical methods, easily defining such complex systems, thanks to its strong mathematical features, allowing to development of different mathematical tools by obtaining algebraic results, and being different from other graphical representation methods. Power losses that occur due to their nature in complex mechanical systems cause uncertainty in these systems. These uncertainties, which cannot be explained by stochastic variability, are defined by "fuzziness". Because some uncertainties in input/output parameters in FMSs arise only from imprecise measurements. In this study, it's thought that the parameters can be integrated into the system after fuzzification by using the "fuzzy set theory" for the uncertainties occurring in the processing times in FMSs. In addition, FMC was modeled with 3 different approaches in order to measure the degree to which the model created using fuzzy parameters reflects the real structure of the system. While no fuzzy parameter was used in the first modeling approach, only some parameters were blurred in the second modeling approach. In the proposed third approach, all uncertain parameters in the model are blurred with the adopted fuzzy set theory. The developed method was used in modeling and performance analysis of an FMC. For this purpose, the MATLAB PN Toolbox program was used. The results showed that the "Fuzzy Transition Timed PN Model", in which fuzzy parameters are used, is the model that most accurately reflects the system.