You can restrict your experimental region to reflect your operating conditions using linear factor constraints or disallowed combinations. In particular, restrictions can be specified for categorical, continuous, and discrete numeric factors. See Define Factor Constraints.

You can explicitly specify your assumed model. Your assumed model is an initial model that ideally contains all the effects that you want to estimate. Your model can contain any combination of main effects, interactions, response surface effects, and polynomial effects (up to the fifth power). You can specify the effects for which estimability is necessary and those for which estimability is desired. Custom Design uses a Bayesian optimality approach to estimate effects whose estimability is desired, subject to the number of runs. See Model.

The Custom Design platform enables you to specify the number of runs that matches the budget for your experimental situation. The platform indicates the minimum number of runs that can be used to estimate the required effects and provides a default number of runs. These values can serve as a guide for determining a feasible number of runs. See Design Generation.

Custom Design can construct a wide variety of design types. These include classical designs and random block designs. For examples of different design types, see the Examples of Custom Designs topic.

See Optimality Criteria.

Designs are constructed using the coordinate-exchange algorithm (Meyer and Nachtsheim, 1995). See Coordinate-Exchange Algorithm.