In the real world, events tend to occur randomly, according to certain statistical patterns or distributions. Distributions allow you to add randomness or variability to your model in order to make it more accurately reflect reality. ProcessModel is capable of creating random sample values that fit a specific theoretical or user-defined distribution.
- You can use a distribution when specifying time values and quantities or when assigning a value to an attribute or variable. For example, this statement:
TIME (T(2, 2.6, 4) min)
would make the activity last for a time randomly selected from a triangular distribution with a minimum time of 2 minutes, a most likely time of 2.6 minutes, and a maximum time of 4 minutes.
Distribution functions are built-in functions that generate random values from numbers using pre-determined patterns. Distributions may be discrete, selecting one among a finite number of possible solutions, or continuous according to the pattern provided by the input parameters. An example of using a discrete distribution is when an entity can randomly route to one of several places, or when randomly generating a batch size. Examples of continuous distributions include service and inter-arrival times. The distributions directly available to you in ProcessModel are: