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Prediction profiller and simulation

Ella

Could you explain what the Black box behind prediction profiller and how it Suggests a solution when we use maximum desirability?

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Mark_Bailey

The desirability functions and optimization are explained here.

Ella

Thank you markbailey, ı have read this information before, but i am trying to understand detail Theory behind the max desirability and simulation. what kind of metodology is jmp Based on when maximum desirability and simulation functions runs?

Mark_Bailey

The general description is:

Optimization Algorithm

Optimization of the overall desirability function, or of the single desirability function if there is only one response, is conducted as follows.

For categorical factors, a coordinate exchange algorithm is used.
For continuous factors, a gradient descent algorithm is used.
In the presence of constraints or mixture factors, a Wolfe reduced-gradient approach is used.

A detailed description is omitted because JMP software is proprietary.

Ella

I see. I try to understand the black box behind simulation because I faced a strange situation when I run simulation experiments a lot of times. (lets say doing run after run) JMP always suggests me different factor levels with smaller defect rates. The number of simulation runs I make is bigger, the better defect rate it suggests me. I don t understand these unstable optimization solutions. Why it does not suggest me global optimum factor levels, how many times do i rerun simulation to get global optimum?