Random and exogenous sources of variation play a central role in discrete- event simulation. Blocks such as the Entity Generator, Value Generator, Server, and Delay blocks usually require a connection to a source of variation. The principal sources of variation are the Numeric Source and Formula blocks. The functionality of these blocks is described in Appendix A: Templates, but this section also provides a quick overview. Both the Numeric Source and Formula blocks provide an OutValue output port to which other blocks can connect to pull numeric values. The values produced by these blocks are dependent on their parameter settings. Figure B.1 shows the Block Properties dialog box for a Numeric Source block with the default settings. The Theoretical option is selected and the Type list box provides a list of the statistical distributions available in Simulation Studio for sampling purposes. You select a distribution from the Type list and then supply the desired parameters in the approripate fields for the distribution that you have chosen. (The details about the distributions available in Simulation Studio are presented later in this appendix.) When a request for a sample comes into the Numeric Source block, the block generates a value based on its parameter settings.
In the Numeric Source block properties, the Fitted option allows you to specify the location of a data set and then JMP is used to automatically fit a theoretical distribution to the data. See Appendix D: Input Analysis, for specific details on using the Fitted option.
The Data Driven option in the Numeric Source block properties has a Type list box that provides you with a variety of methods for generating samples that are based on a specific data set. For example, the Discrete Empirical and the Empirical options are especially useful when it is not possible to find a theoretical distribution that fits the data accurately. Furthermore, the nonhomogeneous Poisson process options NHPP Count and the NHPP Rate allow you to generate a time dependent arrival process that is based on either count or rate data. Finally, the SAS Data Column option can be used to read values from a SAS data set or a JMP data table that are then used directly as a source of input to a simulation model. When using this option in the Numeric Source block, you must supply the file pathname along with the column or variable name in the data set. (See Figure B.2.) Simulation Studio uses the filename extension to determine whether the file is a SAS data set or JMP data table. If a filename extension is not specified, Simulation Studio assumes the file is of the type (SAS data set or JMP table) specified in the Default Data Format section of the SAS Simulation Configuration dialog box.
Another way to introduce random variation into a simulation model is with a Formula block. Figure B.3 shows a sample Block Properties dialog box for the Formula block. You define input variables in the Input Variables area and then use these variables to write an algebraic expression in the Expression area. The values associated with the input variables can come either from ports on the Formula block or from attributes defined on the incoming entity. You can formulate the expression to represent the source of variation that you require. Each time a value is pulled from the Formula block, its expression is evaluated and the resulting value is passed out the OutValue port. See the description of the Formula block in Appendix A: Templates, for additional details about this block.
