What are the Different Types of Simulation Tools?
Computer simulation tools are defined by four sets of characteristics. In most cases, there are two options to choose between, and a choice in one area doesn’t have an impact on choices made in the other areas. This means that types of simulation tools don’t have a set name, but rather a jumble of assigned characteristics. The definitions of these various options define the tool itself, making for a wide variety of choices.
Types of computer simulation tools are defined by several characteristics, and each of them has two main descriptors. The stochastic and deterministic models define the method of imputing data into the simulation. The final output goal of the model is defined by whether it is steady-state or terminating. The difference between a continuous and discrete model is the way information is processed by the simulation as a whole. Lastly, local and distributed models define the method used to organize and run the simulation.
The way data is put into the simulation tools often determines its connection to the real world. If the simulation uses a stochastic model, then it usually attempts to simulate real-world factors. It does this by using a random generator to constantly feed unexpected information into the simulation. In a deterministic simulation, specific information is fed into the model to see the results under specific circumstances.
The final output of simulation tools is usually defined by what is being simulated. In a steady-state model, the simulation can run forever without stopping. These are used to monitor processes without natural stopping points, such as water flowing in a river. A terminating simulation has a natural beginning and end point. A terminating simulation might model the number of people that enter a store on a given day, beginning at store opening and terminating at store closing.
The method a simulation tool uses to handle imputed information is another connection to the nature of the model. In a continuous model, the simulation is always taking in new information and outputting results. A flight simulator is a good example of this; flight information is constantly coming into the system, which requires constant interaction. In a discrete model, the information is all entered, then executed at one time or in predetermined intervals. These models are often used for flaws in testing products and systems.
The last simulation tools option determines how the simulation is organized. In a local simulation, the model runs in a single place, often on a single computer. Distributed models run on a large number of machines, usually on a network or even over the Internet. The reasons to run a simulation over such a wide area usually have to do with computer power—the more machines that run the simulation, the more information it can gather.
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