[jlgray]

I used MiniZinc on the capacitated vehicle routing problem in a combinatorial optimization project course a couple of years ago, and really enjoyed it.

The sweet spot for MiniZinc is in investigating a problem and prototyping a solution. I would say it takes 10-20 hours to get a workable understanding of the language and paradigm, plus or minus your previous experience.

However, MiniZinc is built on backtracking, which scales poorly to real world instances of np-hard problems.

[lambdadmitry]

You can always use MiniZinc (or any other CP solver) to make a "large neighborhood search" where you do local optimization over subproblems, feeding subproblems into the constraint solver. As far as I know, this is one of the most successful approaches for big problems.

[gjem97]

My understanding was that there are many solvers that you can use. Do they all backtrack?

[jsjolen]

The goal of a constraint solver is to bind (constrain) all of the defined variables to a single value which satisfies the constraints put upon that variable.

This is in essence traversing through a search tree (through DFS) and finding a valid end-node.

This means that when we find an invalid end-node (for example by running out of possible values for a variable) then we must in some way go back up the search tree.

There are several different ways of doing this.

Short answer: Yes, we must backtrack somehow.

[CJefferson]

In some vague sense, any solver for an NP-complete problem, must either backtrack, or keep accumulating information and possibly exhaust memory. In practice every practical solver I know of for NP-complete problems backtracks.

Now, they often do all kinds of other clever things on top (learning, restarts, parallelisation, heuristics), but when the going gets tough, there is a lot of backtracking.