And I can absolutely agree that, as more criteria arise, the mechanism for linear scalarization probably becomes more fragile (subject to inconsistent behavior from the coefficients). As a result, something less sensitive but more robust, such as the tiered ordering, is probably preferable. But yeah, we just have not seen the demand yet. What actually seems to be most common is that people who have ~10 metrics spend some time thinking about it, and then realize that they mostly only cared about 1-2 so long as the rest did not cause problems/failures. That was part of the reason I wrote about the epsilon-constraint idea.
We do this in one sense within our company, but it's actually not within the context of a numerical multicriteria optimization problem. We are always trying to optimize around our customer's needs, which is in some ways a multicriteria problem involving balancing: 1) the "best" parameterization of a model subject to some (usually cross-validation) metric, 2) the "cost" (number of samples) required to optimize the model quality, 3) the "robustness" of (degree to which small parameter changes impact) the resulting solution, 4) the "parallel speed" (number of simultaneous suggestions) of the optimization process.
We consult with enterprise customers to understand their needs and expectations regarding these criteria to produce a sort of hierarchical ordering (as you've suggested) which helps inform our optimization procedure (maybe a customer doesn't care as much about speed but definitely cares about robustness). Obviously, it's a relatively restricted problem, and we're not considering it in a rigorous mathematical framework (just how best to serve our customers). Because these factors have no real numerical relationship, the only mechanism we can use to balance the concerns is a relative ordering, which is then manage internally. We spoke about this design at the ICML AutoML workshop this year (A Strategy for Ranking ... at https://sites.google.com/site/automl2016/accepted-papers)