After some quick searching (https://www.princeton.edu/~aaa/Public/Teaching/ORF523/S16/ORF523_S16_Lec16_gh.pdf https://www.princeton.edu/~aaa/Public/Teaching/ORF

After some quick searching (https://www.princeton.edu/~aaa/Public/Teaching/ORF523/S16/ORF523_S16_Lec16_gh.pdf https://www.princeton.edu/~aaa/Public/Teaching/ORF523/S16/OR...), I don't think so. It looks like for robust optimization problem you don't know for certain what the objective function is or have uncertain data measurements for the objective function which I don't think is the case here. I could be completely wrong though. From my understating, if you want to only able to only pick form a discrete set of components for certain variables, you are essentially transforming the problem into a Mixed-Integer Non-Linear Optimization problem (MINLP). I tried to find and easy way to do this with some python optimization libraries but they always needed other packages which were hard to install on Windows. So my solution was to "relax" the discrete constraints by using a continuous approximation of the minimum function when looking at the error from the nearness comment value that is possible. This also lets you assign weighting to have a tradeoff between having realistic comment values vs. having lower error for your main objective.