Different ways to generate and initialize entities and their attribute values.

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.

Have you ever been in a supermarket checkout and wondered why you are in the slowest line? This example shows how queuing systems can be modeled in SimEvents for this type of application. Two parallel versions of a simple model of a four register supermarket counter are presented - one that uses four separate queues and one with a single "serpentine" queue that feeds all registers.

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 central resource that manages distributed processing according to an explicit formula. The example describes a distribution center that manages a series of processes that each job undergoes, with each job carrying information about the series of processes that it must undergo. One of the applications where this could be useful is when you want to model a central dispatcher that routes calls from one call processing station to another.

Create common communication patterns using SimEvents®. In message-based communication models, you can use SimEvents® to model and simulate middleware, and investigate the effects of communication and the environment on your distributed architecture.