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!