JBoss.orgCommunity Documentation

Chapter 1. Planner introduction

1.1. What is Drools Planner?
1.2. What is a planning problem?
1.2.1. A planning problem is NP-complete
1.2.2. A planning problem has (hard and soft) constraints
1.2.3. A planning problem has a huge search space
1.3. Status of Drools Planner
1.4. Get Drools Planner and run the examples
1.4.1. Get the release zip and run the examples
1.4.2. Run the examples in an IDE (IntelliJ, Eclipse, NetBeans)
1.4.3. Get it with maven, gradle, ivy, buildr or ANT
1.4.4. Build it from source
1.5. Questions, issues and blog

Drools Planner optimizes planning problems. It solves use cases, such as:

  • Employee shift rostering: rostering nurses, repairmen, ...

  • Agenda scheduling: scheduling meetings, appointments, maintenance jobs, advertisements, ...

  • Educational timetabling: scheduling lessons, courses, exams, conference presentations, ...

  • Vehicle routing: planning vehicles (trucks, trains, boats, airplanes, ...) with freight and/or people

  • Bin packing: filling containers, trucks, ships and storage warehouses, but also cloud computers nodes, ...

  • Job shop scheduling: planning car assembly lines, machine queue planning, workforce task planning, ...

  • Cutting stock: while minimizing waste: cutting paper, steel, carpet, ...

  • Sport scheduling: planning football leagues, baseball leagues, ...

  • Financial optimization: investment portfolio optimization, risk spreading, ...

Every organization faces planning problems: they have a number of things to do and a limited set of constrained resources to do them with.

Drools Planner enables normal JavaTM programmers to solve planning problems efficiently. Under the hood, it combines optimization algorithms (including Metaheuristics such as Tabu Search and Simulated Annealing) with the power of score calculation by a rule engine.

Drools Planner, like the rest of Drools, is business-friendly open source software under the Apache Software License 2.0 (layman's explanation).

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

  • It's easy to verify a given solution to a problem in reasonable time.

  • There is no silver bullet to find the optimal solution of a problem in reasonable time (*).


(*) At least, none of the smartest computer scientists in the world have found such a silver bullet yet. But if they find one for 1 NP-complete problem, it will work for every NP-complete problem.

In fact, there's a $ 1,000,000 reward for anyone that proves if such a silver bullet actually exists or not.

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

  • A brute force algorithm (even a smarter variant) will take too long.

  • A quick algorithm, for example; in bin packing, putting in the largest items first, will return a solution that is usually far from optimal.

Drools Planner does find a good solution in reasonable time for such planning problems.

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'll 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.

Drools Planner 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. In Drools Planner it is easy to switch the optimization algorithm, by changing the solver configuration in a few XML lines or by API.

Drools Planner is production ready. The API is almost stable but backward incompatible changes can occur. With the recipe called UpgradeFromPreviousVersionRecipe.txt you can easily upgrade and deal with any backwards incompatible changes between versions. That recipe file is included in every release.

You can download a release zip of Drools Planner from the Drools download site. Unzip it. To run an example, just open the directory examples and run the script (runExamples.sh on Linux and mac or runExamples.bat on windows) and pick an example in the GUI:

$ cd examples
$ ./runExamples.sh
$ cd examples
$ runExamples.bat

The Drools Planner jars are available on the central maven repository (and the JBoss maven repository).

If you use maven, just add a dependency to drools-planner-core in your project's pom.xml:


This is similar for gradle, ivy and buildr.

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.

Your questions and comments are welcome on the user mailing list. Start the subject of your mail with [planner]. You can read/write to the user mailing list without littering your mailbox through this web forum or this newsgroup.

Feel free to report an issue (such as a bug, improvement or a new feature request) for the Drools Planner code or for this manual to the drools issue tracker. Select the component drools-planner.

Pull requests (and patches) are very welcome and get priority treatment! Include the pull request link to a JIRA issue and optionally send a mail to the dev mailing list to get the issue fixed fast. By open sourcing your improvements, you 'll benefit from our peer review, improvements made upon your improvements and maybe even a thank you on our blog.

Check our blog and twitter (Geoffrey De Smet) for news and articles. If Drools Planner helps you solve your problem, don't forget to blog or tweet about it!