All the use cases above are probably NP-complete or harder. In layman's terms, NP-complete means:

The implication of this is pretty dire: solving your problem is probably harder than you anticipated, because the 2 common techniques won't suffice:

By using advanced optimization algorithms, OptaPlanner does find a good solution in reasonable time for such planning problems.

Usually, a planning problem has at least 2 levels of constraints:

Some problems have positive constraints too:

Some basic problems (such as N Queens) only have hard constraints. Some problems have 3 or more levels of constraints, for example hard, medium and soft constraints.

These constraints define the score calculation (AKA fitness function) of a planning problem. Each solution of a planning problem can be graded with a score. With OptaPlanner, score constraints are written in an Object Oriented language, such as Java code or Drools rules. Such code is easy, flexible and scalable.

A planning problem has a number of solutions. There are several categories of solutions:

Counterintuitively, the number of possible solutions is huge (if calculated correctly), even with a small dataset. As you can see in the examples, most instances have a lot more possible solutions than the minimal number of atoms in the known universe (10^80). Because there is no silver bullet to find the optimal solution, any implementation is forced to evaluate at least a subset of all those possible solutions.

OptaPlanner supports several optimization algorithms to efficiently wade through that incredibly large number of possible solutions. Depending on the use case, some optimization algorithms perform better than others, but it's impossible to tell in advance. With OptaPlanner, it is easy to switch the optimization algorithm, by changing the solver configuration in a few lines of XML or code.

To try it now:

The Examples GUI application will open. Pick an example to try it out:

Besides the GUI examples, there are also a set of webexamples to try out:

Pick an example to try it out, such as the Vehicle Routing example:

To run the examples in your favorite IDE:

To run a specific example directly and skip the example selection window, run its App class (for example CloudBalancingApp) instead of OptaPlannerExamplesApp.

The OptaPlanner jars are also available in the central maven repository (and also in the JBoss maven repository).

If you use Maven, add a dependency to optaplanner-core in your project's pom.xml:

    <dependency>
      <groupId>org.optaplanner</groupId>
      <artifactId>optaplanner-core</artifactId>
    </dependency>

This is similar for Gradle, Ivy and Buildr. To identify the latest version, check the central maven repository.

Because you might end up using other OptaPlanner modules too, it's recommended to import the optaplanner-bom in Maven's dependencyManagement so the OptaPlanner version is specified only once:

  <dependencyManagement>
    <dependencies>
      <dependency>
        <groupId>org.optaplanner</groupId>
        <artifactId>optaplanner-bom</artifactId>
        <type>pom</type>
        <version>...</version>
        <scope>import</scope>
      </dependency>
      ...
    </dependencies>
  </dependencyManagement>

If you're still using ANT (without Ivy), copy all the jars from the download zip's binaries directory in your classpath.

It's easy to build OptaPlanner from source:

OptaPlanner is:

We release a Beta or CR version every few weeks and a Final version every few months. Read the release notes of each release on our website.

OptaPlanner separates its API and implementation:

For news and articles, check our blog, Google+ (OptaPlanner, Geoffrey De Smet) and twitter (OptaPlanner, Geoffrey De Smet). If OptaPlanner helps you, help us by blogging or tweeting about it!

Public questions are welcome on our community forum. Bugs and feature requests are welcome in our issue tracker. Pull requests are very welcome on GitHub and get priority treatment! By open sourcing your improvements, you 'll benefit from our peer review and from our improvements made on top of your improvements.

Red Hat sponsors OptaPlanner development by employing the core team. For enterprise support and consulting, take a look at the BRMS and BPM Suite products (which contain OptaPlanner) or contact Red Hat.

OptaPlanner is part of the KIE group of projects. It releases regularly (often once or twice per month) together with the Drools rule engine and the jBPM workflow engine.

See the architecture overview to learn more about the optional integration with Drools.