It's the only framework of its kind for constraint optimization. Nothing else comes close to the flexibility at high level Optaplanner offers. I would bet over 50% of commercial software that does optimization over NP-hard problems is using optaplanner.
- ConstraintsStreams (instead of DRL) to declare incremental constraints easily in plain Java (or another JVM language). No need to learn the Drools Rule Language any more.
- Autoconfiguration (no need for solverConfig.xml unless you want to power tweak) thanks to our Quarkus extension or Spring Boot starter.
- SolverManager that manages Solver threads for you. Great to avoid HTTP REST timeouts or to solve multiple problems.
Coming soon:
- Code completion (XSD) when power tweaking the solverConfig.xml
- Simplified VRP model
My suggestion is to create a simple way to deploy a to containers so that a primary server can pass out parallel optimization runs to a set of secondary nodes, allowing you to parallelize really easily.
a second generation of this would include code to spin up secondary nodes on demand depending on the number of parallel scenarios you want to run.
Today, it should already just be a matter of taking the quickstart(s): https://github.com/kiegroup/optaplanner-quickstarts and following the quarkus deploy guide: https://quarkus.io/guides/deploying-to-openshift but we'll streamline it even more.
As a second stage goal, this will deal with starting up nodes as needed too.
Another hard part was combining chain through time with entities that can be scheduled together vs ones that need gaps. This seems to be a common use case, grouping work where you need a setup/teardown gap between entities. Not sure how to improve this but maybe add some kind of built-in support for "entity coloring" on chained variables where you can configure which run together in a chain vs which ones need setup/teardown gaps between.
Overall Optaplanner is an amazing piece of software though, good work!
On the commercial NP-hard optimization front Gurobi, CPLEX and to some extent XPRESS are surely formidable as well. Of course mixed-integer solvers don't always directly compete with constraint programming solvers, since some problems are better solved with one or the other kind of solver.
YMMV, I am very biased.
It's more automatic and focuses on good performance out of the box, with custom solver phases and such only recommended if you have performance problems.
Its also fully object oriented. The solver works on Java POJO's
In addition, it has built in tools for unit testing and detecting score corruption.
It's very much on the practical, rather than theoretical end of solvers. You can drop it into an existing Java project with zero fuss.