Modeling of complex structured processes using discrete iterative networks and petri nets

Abstract

Abstract The paper considers the use of discrete iterative networks and Petri nets in the modeling of complex structured processes. In general, the production process is represented as a composition of machines. Probabilistic finite machine are modeled to select optimal solutions from a certain set of alternative solutions. Alphabets of finite and probabilistic machines are formed on the basis of discrete optimization methods when modeling multi-stage productions. The processing mode adaptation block is used to change the alphabets when the production conditions are changed. Using the alphabet generation block allows you to choose the optimal value of the alphabets of the random variables under study. During modeling complex systems and developing algorithms for managing them, the presence of ambiguous functional relationships between factors and quality indicators is taken into account. Methods of modeling complex spatially distributed objects based on a hierarchy of machines belonging to a predetermined finite number of machine types using iterative networks and Petri nets are described.