python-mip VS OptaPlanner

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.

However, if you need to solve constraints etc., see: https://www.optaplanner.org/

Try looking for something built around Optaplanner - basically taking the end game of rostering and working backwards.

OptaPlanner takes some of the items tracked in tool like Odoo and creates plans based on them. I.E. how do you manage shifts while juggling multiple constraints (Jerry can only work weekends, Jeff can only work afternoons, Jim can work weekends but only on double time, etc.)

There is a library out there called Optaplanner that is designed for optimization of NP complete problems. It is hard to tell if that is exactly what this is, but I think you should be able to use this regardless.

Just stumbled upon https://www.optaplanner.org. Looks very interesting but also really overkill.

OptaPlanner - a generic scheduler.

OptaPlanner

I've never used it but it sounds like OptaPlanner may be a scheduler in the same vein?