alt Hacker NewsAlthough this post discusses Constraint Programming - Satisfiability (CP-SAT) Solvers and Mixed Integer Problem (MIP) Solvers, it does not discuss Metaheuristic Solvers.
Metaheuristic solvers are different in that you don't need to model your problem as a mixed integer problem. Instead, all it cares about is having a function that returns something you can compare. This allows you to model your problem however you like. Some metaheurstic techniques include Simulated Annealing, Late Acceptance Local Search, and Tabu Search.
Metaheuristic solvers may not generate optimal solutions (after all, by their nature, they don't know the structure of the problem), but they generate "good enough" and "close to optimal" solutions. Metaheurstic solvers tends to beat MIP and CP-SAT for VRP, whereas MIP and CP-SAT are better for bin packing.
If you want to try using a Metaheuristic solver, I can recommend Timefold, which allows you to define your constraints using your domain objects in an incremental matter (it has SQL/Java-Streams like syntax, which in my opinion, is more readable than formulas) (disclosure: I work for Timefold).
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I met some lovely Timefold folks at the Informs conference, and I appreciate the work you do. Any idea when a port is coming to new languages?
Interesting! Could you give example of problems you're using this for, to get an idea of where they'd be best suited?
Would be real interested in hearing any known gotchas or best practices that might be less commonly known here. I’m recently been working on a (fairly low set of dimensions) VRP problem for a service, but we need solutions faster than my initial exploration with OR Tools could yield. (Think multiple permutations of a route within a few seconds). I’ve heard Timefold come up before, being able to model via a function is a plus.