Hi Craig Hales,
A query, related to multi-objective optimization... JMP will be able to optimize the uncertainty of random discrete inputs (integer numbers) in "1 single step"?...that is, JMP will be able to find the best combination of inputs to use (integer numbers) And the risk associated with each strategy?...and thus be able to look for strategies that allow minimizing the risks while achieving the objectives?
That is, take any optimization problem and have JMP replace the uncertain values by probability distribution functions (integers - discrete) that represent a range of possible values and for each trial solution that JMP finds during optimization, JMP run a Monte Carlo simulation, finding the combination of adjustable cells that provides the best simulation results that minimize risk in the face of uncertainty?.
Cheers,
Marco