A hybrid system with both continuous time and discrete event sections. The discrete event part models tanks, represented by entities, which are being queued and need to be filled up. Each tank has a "Capacity" attribute. The continuous time part models the process of filling up a tank, modeled by an Integrator. When a tank is filled to capacity, this event can be detected by a Hit Crossing block, which will generate a message corresponding to this event. The generated message will trigger the server to release the tank.

Model a single-queue single-server system with a single traffic source and an infinite storage capacity. In the notation, the M stands for Markovian; M/M/1 means that the system has a Poisson arrival process, an exponential service time distribution, and one server. Queuing theory provides exact theoretical results for some performance measures of an M/M/1 queuing system and this model makes it easy to compare empirical results with the corresponding theoretical results.

Model a single-queue single-server system that has a Poisson arrival process and a server with constant service time. The queue has an infinite storage capacity. In the notation, the M stands for Markovian; M/D/1 means that the system has a Poisson arrival process, a deterministic service time distribution, and one server.

Model a single-queue single-server system in which the interarrival time and the service time are uniformly distributed with fixed means of 1.1 and 1, respectively. The queue has an infinite storage capacity. In the notation, the G stands for a general distribution with a known mean and variance; G/G/1 means that the system's interarrival and service times are governed by such a general distribution, and that the system has one server. You can change the variances of the uniform distributions. You can use this model to examine Little's law.