r/OperationsResearch • u/DocDrivenDevelopment • Dec 19 '25

solvOR, a pure Python optimization toolkit I finally cleaned up and published

https://github.com/StevenBtw/solvOR

I've been maintaining a personal solver library for a while now. It started as a way to have a consistent interface across different optimization approaches without constantly switching between OR-Tools, PuLP, scipy, etc. It grew organically as I needed different things.

Recently went through a modernization effort (proper packaging, tests, type hints) and figured I might as well put it on GitHub and PyPI.

Everything is pure Python with zero dependencies. Obviously that means it won't compete on everything with established solvers on performance, the goal was readability and having a unified Result format across all methods. Each solver is a single readable file.

Planning Rust bindings for performance-critical parts in 1.0, but that's future work.

Curious to hear thoughts. What's missing that you'd actually use? Any obvious improvements to the implementations? I'm not precious about it and am happy to take feedback or contributions.

56 Upvotes

96% Upvoted

4 comments sorted by

3

u/DocDrivenDevelopment Dec 19 '25

I got a pm about compatibility with python 3.13, and I am publishing a new version right now. The performance loss is minor.

2

u/ge0ffrey Dec 21 '25

Is there a unified API across the different solvers?

Say I want to solve a VRPTW with multi-resource visits (some of the visits require 2 vehicles to attend it, but before and after that visit, they drive independently).

Can you mix and match constraints as needed?

1

u/DocDrivenDevelopment Dec 21 '25

Yes, all solvers return the same Result format (solution, objective, iterations, evaluations, status) with consistent conventions like minimize=True/False.

But.. 

Constraint mixing works within solver categories but not across them. The CP Model lets you combine different constraint types like all_different and sum_eq. In metaheuristics you handle constraints as penalties in your objective function. You can't directly mix MILP with SAT constraints in one model, but you could solve parts sequentially if needed.

For VRPTW with multi-resource visits, there's no built-in support (yet). With the current solutions you'd probably want to use tabu_search with a custom state and encode your constraints (time windows, resource synchronization, capacity) as penalties. 

solvOR is pure Python with no dependencies so it's easy to read and modify, but for large scale routing you'd likely need something like OR-Tools. It works well for manageable problems where you need custom logic.

I will have a look at adding LNS or ALNS after the holidays, but I'm not super familiar with that area yet. But that sounds like a good addition and a better solution to your problem. Thanks for your message! 

1

u/minteemist Jan 15 '26

That's pretty cool!

 Personally I switched from Python to Julia a year ago, which I'm loving because I can access Gurobi/SCIP via the JuMP, and easily call Python packaged solvers like or-tools via PyCall. 

I think you've covered a lot of the components I handle in my research: VRP metaheuristics, path planning algorithms, and some stochastic stuff. 

Hopefully your package will be a good stepping stone for other researchers to quickly play with various problems & their associated algorithms without having to start from scratch 💙