We used it to create models of networked biological systems (e.g. the immune system, gene regulatory networks, etc.). The network topology was constructed from mined academic literature and then the model was constrained using data. The great thing about using MiniZinc was that it could propose multiple models with the same or similar “accuracy” to the data. You would then have an ensemble of models from which to make predictions (i.e. how does a drug affect the system).
We would have MiniZinc files that were 10s or 100s of MBs and would run for days. Sometimes they would be unsatisfiable within a few seconds, other times within hours. Many of the solvers are single threaded, and those that are multithreaded don’t seem to be much more efficient with more cores.
MiniZinc is very difficult to debug and the paradigm shift can be jarring coming from procedural languages. However, I am grateful it exists and for the work that many have put into it. Similar to the train yard comment, we used Python to construct the MiniZinc models and then submitted them to the solvers via the minizinc-python package which is well written and meek maintained.