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Similarly, if you are interested in aggregate values, and not individual unit interaction, system dynamics may be applied. For example, if it is easier to describe the behavior of each individual object than trying to create a global workflow, agent based modeling may be the solution. However, it is not always clear which of the three modeling paradigms is best for a system.
![discrete event simulation discrete event simulation](https://www.mathworks.com/help/simevents/gs/transmitter_receiver_example.png)
When the system under analysis can naturally be described as a sequence of operations, discrete event modeling techniques should be used. Read the white paper Discrete event modeling and multimethod simulation Read the white paper and see why hybrid models are always a better choice! In our white paper, Multimethod Simulation Modeling for Business Applications, we investigate three main simulation modeling approaches: discrete event, agent based, and system dynamics modeling, and construct a multimethod model example to illustrate the advantages of combining different methods. Discrete event simulation modeling is widely used in the manufacturing, logistics, and healthcare fields. Typically, specific physical details, such as car geometry or train acceleration, are not represented. These events and movement between them can be smoothly animated.ĭiscrete event simulation focuses on the processes in a system at a medium level of abstraction. The actual movement of the train would be modeled as a time delay between the departure and arrival events. Using discrete event simulation modeling, the movement of a train from point A to point B is modeled with two events, namely a departure and an arrival.
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To simulate this, discrete event simulation is often chosen. For example, a truck arrives at a warehouse, goes to an unloading gate, unloads, and then departs. Most business processes can be described as a sequence of separate discrete events.