JMP Community members are happy to help but be warned that DOE is a broad and deep subject. It is difficult to convey knowledge about it in a few replies.
Custom Design is unique to JMP but other software implements 'optimal design' methods including the 'coordinate exchange' algorithm. The older methods were based on the technology at hand: paper and pencil and mathematics. It was based on combinatorics and group theory. It was largely from a 'design-centric' view. A new view developed in the 1970s that was more of a 'model centric' view. It is based on an optimality criterion and numerical solution. There are many criteria that can be used. You mention D-optimal and I-optimal designs. D-optimal designs minimize the confidence region of the estimates of the model parameters. This result is best when you are most interested in inference about the estimates. (That is, are they significantly different from zero?) I-optimal designs minimize the integrated variance of estimates of the response. (That is, the model predictions have the smallest confidence intervals.)
So you get to choose the way in which the design is optimal. You also get to specify any linear model, if any terms must be estimated or not, aliasing to be avoided, fixed or random blocking, restricted randomization, and the number of runs. You cannot do any of that with either of the two classical designs that you mentioned.
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