All the use cases above are probably NP-complete. In layman's terms, this 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, Planner 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 Planner, score constraints are written in an Object Orientated 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 Planner, 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. Just pick an example:

To run the examples in your favorite IDE:

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 and manually verify that your classpath doesn't contain duplicate jars.

You can also easily build OptaPlanner from source yourself.

Set up Git and clone optaplanner from GitHub (or alternatively, download the zipball):

$ git clone git@github.com:droolsjbpm/optaplanner.git optaplanner
...

Then do a Maven 3 build:

$ cd optaplanner
$ mvn clean install -DskipTests
...

After that, you can run any example directly from the command line, just run this command and pick an example:

$ cd optaplanner-examples
$ mvn exec:exec
...