ryan-nextmv

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Even if this paper is correct, the approach is not practical. From table 1 in section 6.2, the LP representation of a 25-city TSP requires 24,580,032 variables and 3,643,825 constraints. Merely formulating that model will require significantly more time than fully optimizing a TSP of that size using an assignment or 2-matching relaxation and adding subtour elimination constraints as they are violated. The former will likely take seconds (or maybe minutes) at that size, while the latter can be accomplished in milliseconds.

Yes! MiniZinc is great. It's the only sticker I have on my Linux box.

I like how it targets both MIP and CP solvers. It's actually the tool that first led me to CP and Gecode.

Is it safe to say those optimizations are performed ad hoc? That is, you're manually in control of the optimization process instead of wiring it up to some kind of automation?

<3 SCIP. About a dozen years ago I used to write the python-zibopt library (now defunct and rendered moot by the far superior PySCIPOpt).

Hadn't heard of JoD - that looks really interesting. What are your experiences so far?

"My problem is mind-numbingly complex, like everyone else doing supply-chain optimization" brings me joy. I'll definitely reach out - would love to get your reaction to some of our new ideas.

Thanks for looking at our docs in light of your problem domain. That's way beyond what I anticipated, and much appreciated!

We haven't put a lot more examples into our SDK docs lately since we've been working more on our routing engine and cloud offering. Now we're getting back to more foundational questions like "what should our solver be?" and "how should model formulation work?"

Hop started off as a decision diagram solver, but even internally we strap a lot of different techniques onto it. My hope is to support those patterns better, which is really why I posed this question.

I'd be interested to know: what made the process of converting business logic into solver-speak painful?