I compare the strengths and weaknesses of paradigms and approaches within those paradigms for solving machine scheduling problems. The amount of real-world data I have is limited and (random) subsampling critically changes the characteristics of the sets so that they are no longer realistic. I do however feel it is necessary to provide a richer class of problem data as to see how different methods react to different intrinsics - as long as they are likely to be more than just theoretical. What is the most responsible and honest way to include this in my research?
There are papers mentioning typical industry ranges for certain characteristics, e.g. a rate for how tight deadlines are. Therefore, my best idea so far is to do carry out a (full) factorial analysis for those parameters. Other ratios also seem interesting, such as average transition vs. execution duration, but I don't exactly find resources mentioning them or generators for creating them.
I feel like I'm not being rigorous enough if I just disregard the part of research just mentioned, but at the same time, I don't want to muddy the waters by introducing irrelevant parameters. Preliminary results show that indeed, for the same problem formulation, the best result sometimes depends on the shape (and obviously, the size) of the data set.