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First, the advantages. Simulations can help in cases where mathematical models are not applicable, because the system to be modeled is either too complex, the system's behavior cannot be expressed by mathematical equations, or the system involves uncertainty, i.e. stochastic elements. In either of these cases, simulation can be applied as a last resort to gain information about the system.

Simulation is especially useful if changes in an existing system are to be made, and the effects of the changes should be tested prior to implementation. Trying out the changes in the real system may not be an option, because the system does not yet exist, the costs are too high, there are too many scenarios to test, the test would take too much time (weeks, months, years), the changes are not legal, etc. In all these cases, a simulation model allows to test various scenarios in often only a couple of minutes or hours.

A disadvantage of simulation in comparison to exact mathematical methods is that simulation cannot naturally be used to find an optimal solution. There are methods which long to optimize the result, but simulation is not inherently an optimization tool. Simulation is often the only means to approach complex systems analysis. Many systems cannot be modeled with mathematical equations. Simulation is then the only way to get information at all.

Another disadvantage is that it can be quite expensive to build a simulation model. First, the process that is to be modeled must be well understood, although a simulation can often help to understand a process better. The most expensive part of creating a simulation model is the collection of data to feed the simulation, and to determine stochastic distributions (e.g. processing times, arrival rates etc.).

Another key point is to ensure the model is valid, i. e. it's behavior mirrors that of the original (physical) system. For systems that don't exist yet, because simulation is used for planning it, this is especially hard. Unsufficient validation and verfication of a simulation model is one of the top reasons for failing simulation projects. The consequence is false results, and this lessens the credibility of the method in general.

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15y ago

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