Decision variables are variables in your model that you can control, such as how much rent to charge or how much money to invest in a mutual fund. Decision variables aren’t required for Crystal Ball models, but are required for OptQuest models. You define decision variables in Crystal Ball using Define, Define Decision or by clicking the Define Decision button in the toolbar or Microsoft Excel 2007 or later ribbon.
When you define a decision variable in Crystal Ball, you define its:
Bounds—Defines the upper and lower limits for the variable. OptQuest searches for solutions for the decision variable only within these limits.
Type—Defines whether the variable type is discrete, continuous, binary, category, or custom:
Continuous — A variable that can be fractional (that is, it is not required to be an integer and can take on any value between its lower and upper bounds; no step size is required and any given range contains an infinite number of possible values.
Discrete — A variable that can only assume values equal to its lower bound plus a multiple of its step size; a step size is any number greater than zero but less than the variable's range.
Binary — A decision variable that can be is 0 or 1 to represent a yes-no decision, where 0 = no and 1 = yes.
Category — A decision variable for representing attributes and indexes; can assume any discrete integer between the lower and upper bounds (inclusive), where the order (or direction) of the values does not matter (nominal). The bounds must be integers.
Custom — A decision variable that can assume any value from a list of specific values (two values or more). You can enter a list of values or a cell reference to a list of values in the spreadsheet. If a cell reference is used, it must include more than one cell so there will be two or more values. Blanks and non-numeric values in the range are ignored. If you enter values in a list, they should be separated by a valid list separator -- a comma, semicolon, or other value specified in the Windows regional and language settings.
Step Size—Defines the difference between successive values of a discrete decision variable in the defined range. For example, a discrete decision variable with a range of 1 to 5 and a step size of 1 can only take on the values 1, 2, 3, 4, or 5; a discrete decision variable with a range of 0 to 2 with a step size of 0.25 can only take on the values 0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, and 2.0.
The cell value becomes the base case value, or starting value for the optimization.
If changing the type of a decision variable causes the base case to fall outside the range of values that are valid for that type, a new base case value is selected. The base case changes to the nearest acceptable value for the new type. |
In an optimization model, you select which decision variables to optimize from a list of all the defined decision variables. The values of the decision variables you select will change with each simulation until the best value for each decision variable is found within the available time or simulation limit.