Single vs Multiple deep learning networks for multi-label classification?
This kind of setup is normally addressed with a single neural network that has an output sigmoid layer of $N$ units (where $N$ is the number of possible fixes).

Having combinations of fixes represented with their own label does not make much sense; instead, let the different labels take the expected value: if 2 fixes are fine, let both expected outputs be 1.

Having $N$ networks each one generating a single fix-nofix is equivalent but does not benefit from the potential gains of the multitask learning obtained when training everything together and obtaining a shared internal representation.