Hi @noahsprent,
If you have other factors to add (that are not mixture factors), you will have to switch to Custom Design indeed.
The number of levels for mixture factors in the design will highly depend on the supposed model : main effects, interactions, higher power effects, scheffe cubic interactions, etc... with an influence of the Optimality Criterion (for the number of runs at intermediate levels vs. min/max levels).
But choosing a D-Optimal design (in the red triangle of "Custom Design", "Optimality Criterion", "Make D-Optimal Custom Design") and specifying only main effects and 2-factors interactions in the model should give you a similar type of design.
Example here by adding a continuous factor ProcessVariable1 (range between 50 and 100, datatable attached) :
And JSL script for DoE creation :
DOE(
Custom Design,
{Add Response( Maximize, "Y", ., ., . ), Add Factor( Mixture, 0.5, 1, "A", 0 ),
Add Factor( Mixture, 0, 0.5, "B", 0 ), Add Factor( Mixture, 0, 0.5, "C", 0 ),
Add Factor( Continuous, 50, 100, "ProcessVariable1", 0 ),
Set Random Seed( 1119139817 ), Number of Starts( 25456 ), Add Term( {1, 1} ),
Add Term( {2, 1} ), Add Term( {3, 1} ), Add Term( {1, 1}, {2, 1} ),
Add Term( {1, 1}, {3, 1} ), Add Term( {1, 1}, {4, 1} ),
Add Term( {2, 1}, {3, 1} ), Add Term( {2, 1}, {4, 1} ),
Add Term( {3, 1}, {4, 1} ), Set Sample Size( 12 ),
Optimality Criterion( "Make D-Optimal Design" ), Simulate Responses( 0 ),
Save X Matrix( 0 ), Make Design}
)
You should be able to have this similar type of design by adding any other type of factors as long as you're creating a D-Optimal design with only 2-factors interactions in the model.
I hope this complementary answer will help you,
Victor GUILLER
L'Oréal Data & Analytics
"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)