[Karrot_Kream]

> extremely niche "CP solver packages and OR-tools" (which one exactly?) significantly reduces project risks such as not being able to find experts, the system not meeting functional or performance requirements, or becoming obsolete down the road. The same cannot be said for some mythical "CP solver packages and OR-tools"

"Mythical CP solver packages and OR-tools"? Lol!

Google's OR-Tools [1] has been winning golds in the MiniZinc Challenge since 2013 [2]. In 2023 it won Gold in most categories and the only Prolog to win, SICStus Prolog, took one Silver back. I'm curious where you're spending time in the community if you can word a comment like this as OR-Tools is the behemoth in this area and Prolog is the weird hacker siren call that appeals to people who certainly aren't about to publish anything novel in the space.

I say this as someone who has tinkered a lot with Prolog over the years and finds Prolog's execution model to work really well with the way I think about programming. Prolog and its hybrid solver model just isn't good enough at any one thing to make it SOTA. It's fun to tinker with, but I just don't think in 2024 it has enough to offer anyone who's not interested in the language or the WAM to make it worth exploring, especially not as an embedded constraint solver.

[1]: https://developers.google.com/optimization/

[2]: https://www.minizinc.org/challenge/

[tannhaeuser]

Ok but is the argument now just because it's by Google with its media presence it's not niche? "Constraint solving" is an overly broad term that can encompass most of computer science but at least finite domain solving, interval propagation, and SAT solving as specific algorithmic approaches with very different applications. Going by your post, potential users looking for planning, scheduling, or optimization problems to solve will jump to very specific implementation techniques having won synthetic benchmarks in basically unrelated domains for showcasing "something with constraints". Classic OR is about optimizing systems of linear (in-)equations, but discrete optimization problems and most financial investment planning problems don't come in this flavor and really need very laborious encodings (like into equational systems with 1000s of artificial variables) to fit OR model formulation requirements or SAT checkers. Great, now you're prematurely solving idiosyncratic representation problems; your consultants surely will rub their hands. But I stand by my opinion that Prolog is, by far, a much better starting point for the kind of explorative programming required in this domain. Making Prolog fast/scaling on mainstream cloud hardware (like Quantum Prolog and SICStus are doing) has very much to offer to users, and is behind many or even most real-world scheduling and optimization applications.