python-mip VS minizinc-python

I've been using CBC via python-mip (https://github.com/coin-or/python-mip). It's great because it's got a super clean interface (milp variables/expressions/constraints), the code is quite accessible, and it's low overhead which makes it good for solving many very small problems.

Community sentiment seems to be beginning to shift toward favouring the HiGHS solver (https://github.com/ERGO-Code/HiGHS) over CBC. Something I'm keeping a close eye on.

nextmv seems to pitch itself as a generic solving ("decision automation") platform or something (unclear). But it seems that the only fleshed out product offering is for vehicle routing, based on the docs. Are there plans to offer, for instance, a solver binary that can be used to solve generic problems?

Also all the github repos under https://github.com/nextmv-io are private, so links from docs are 404.

Now, you say you need to solve this using Python. I would personally either use the MiniZinc Python driver or I would use the OR-Tools Python package and write a similar model to the one above.

Btw, MiniZinc team recently released a python binding https://github.com/MiniZinc/minizinc-python which is quite nice to use.

minizinc-python solves the second issue, by providing the way to call minizinc models from python, the library will run minizinc, serialize your input and parse output, but the programmer still should write quite a lot lines of code. We can look at the example of solving square equation solving: