Hierarchical Modeling of Stochastic Networks, Part I: Fluid Models

摘要: Decision processes in complex operations are frequently hierarchical. It is natural, therefore, for models that support such decisions to be hierarchical as well, and here we explore a hierarchy. Specifically, concerned with stochastic flow networks, typified by queueing which will analyzed within framework distinguishes three aggregation levels of time state-space: microscopic level acknowledges individual particles (which the at network typically set up), macroscopic level, approximated deterministic fluid model, an intermediate mesoscopic quantifies deviations between micro macro terms diffusion approximations. might useful start example, closed nonparametric Jackson network, attempts concretize these levels.