An analytic finite capacity queueing network model capturing the propagation of congestion and blocking

Analytic queueing network models often assume infinite capacity queues due to the difficulty of grasping the between-queue correlation. This correlation can help to explain the propagation of congestion. We present an analytic queueing network model which preserves the finite capacity of the queues and uses structural parameters to grasp the between-queue correlation. Unlike pre-existing models it maintains the network topology and the queue capacities exogenous. Additionally, congestion is directly modeled via a novel formulation of the state space of the queues which explicitly captures the blocking phase. The model can therefore describe the sources and effects of congestion. The model is formulated for networks with an arbitrary topology, multiple server queues and blocking-after-service. It is validated by comparison with both pre-existing methods and simulation results. It is then applied to study patient flow in a network of units of the Geneva University Hospital. The model has allowed us to identify three main sources of bed blocking and to quantify their impact upon the different hospital units.