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Release Notes 6.4

We are happy to announce a 6.4 Final release of OptaPlanner. OptaPlanner is a lightweight, embeddable planning engine written in Java™ to solve AI constraint optimization problems efficiently. Use cases include Vehicle Routing, Employee Rostering, Maintenance Scheduling, Task Assignment, School Timetabling, Cloud Optimization, Conference Scheduling and many more.

Download
Download
8.1.0.Final
Documentation
Documentation
8.1.0.Final
Status of OptaPlanner
  • Stable: Lots of unit, integration and stress tests
  • Reliable: Used across the world in production
  • Scalable: To billions of constraint matches with minimal RAM and CPU time
  • Documented: Read the detailed reference manual and the many examples
  • Open Source: Apache License 2.0
Note for Red Hat Decision Manager customers

The RHDM version differs from the OptaPlanner version:

RHDM version OptaPlanner version
7.7 7.33
7.8 7.39
7.9 7.44

New and noteworthy

Generic API

The SolverFactory and Solver now optionally support a generic type parameter, to avoid the awkward cast to your Solution implementation:

    // Build solver
    SolverFactory<CloudBalance> solverFactory = SolverFactory.createFromXmlResource(
            "org/.../cloudBalancingSolverConfig.xml");
    Solver<CloudBalance> solver = solverFactory.buildSolver();

    // Solve it
    CloudBalance solvedCloudBalance = solver.solve(unsolvedCloudBalance);

The old style still works too, of course.

Statistical benchmarking

Statistical benchmarking allows you to rerun your single benchmarks multiple times. This can be useful to eliminate influences of the environment and verify the results of your benchmarks.

The report visualizes the results of the runs in the "Best score" table, and also compares the results of sub single benchmarks for each problem benchmark in a box plot chart:

sub single benchmark summary statistic

To enable it, add the following line with a positive integer as a parameter to your <inheritedSolverBenchmark> or to individual <solverBenchmark> elements.

<inheritedSolverBenchmark>
  <name>...</name>
  <solver>
    ...
  </solver>
  <subSingleCount>N</subSingleCount>
</inheritedSolverBenchmark>

Nearby Selection memory consumption reduced

When Nearby Selection is used in Local Search, it’s memory consumption is heavily reduced, especially when scaling out:

Nearby Selection memory consumption from 6.3 to 6.4

It can now scale above 10k+ entities on normal hardware, as long as any distributionSizeMaximum is configured.

Furthermore, it’s bootstrap performance is much better too for Local Search.

Kjar and KieContainer support

OptaPlanner now supports kjars, so it can consume a kjar produced by OptaPlanner Workbench or deploy a kjar to OptaPlanner Execution Server at runtime.

A kjar can now contain Solver configurations and the SolverFactory can now load them from it with a KieContainer. This allows to load a solver configuration or score rules at runtime that are not in the original classpath:

        KieServices kieServices = KieServices.Factory.get();
        ReleaseId releaseId = kieServices.newReleaseId("org.nqueens", "nqueens-kjar", "1.0.0");
        KieContainer kieContainer = kieServices.newKieContainer(releaseId);
        SolverFactory<NQueens> solverFactory = SolverFactory.createFromKieContainerXmlResource(
                kieContainer, "org/nqueens/solverConfig.solver");
        Solver<NQueens> solver = solverFactory.buildSolver();

A ksession defined in META-INF/kmodule.xml can now be referenced from a Solver configuration:

<solver>
  ...
  <scoreDirectorFactory>
    ...
    <ksessionName>nqueensKsession</ksessionName>
  </scoreDirectorFactory>
</solver>

New example: Meeting scheduling

Assign meetings of different durations to starting times and rooms.

Also see the blog about time scheduling design patterns.

Other improvements

  • Benchmark warm ups now run in parallel threads, just like the actual benchmarks.

  • Apache Camel’s camel-optaplanner 2.16 supports async solving, daemon mode and ProblemFactChange processing. Contributed by Bilgin Ibryam.

  • A SolverFactory can now be cloned with cloneSolverFactory() to concurrently configure it dynamically per user request without parsing the solver config XML each time.

  • The examples application has switched to a more modern look and feel.

  • Several design patterns to deal with scheduling in time have been documented in the manual.

  • New Construction Heuristic: Allocate To Value From Queue

  • Various bugs resolved. See our issue tracker.

Workbench and Execution server

As mentioned in our blog, we 've build first version of OptaPlanner Workbench and OptaPlanner Execution Server. Take a look at what it can do:

To try out OptaPlanner Workbench, download and deploy the KIE Workbench war and create a user with the role plannermgmt. Note that a user with the admin role currently doesn’t see OptaPlanner screens yet.

To try out OptaPlanner Execution Server, download and deploy the KIE Execution Server war and use the KIE and OptaPlanner REST API (both are currently documented in the drools documentation). It’s enabled by default (although it can be disabled with a configuration property).

Upgrade your code to 6.4

The best and easiest way to upgrade to this new version of OptaPlanner is by following the upgrade recipe.

New features in older releases

Read the previous release notes to learn about the new and noteworthy in previous releases.

Comments

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Latest release
  • 8.1.0.Final released
    Fri 15 January 2021
Paid support and consulting

Want to talk to the experts? Red Hat offers certified binaries with enterprise consulting. Contact optaplanner-info for more information.

Upcoming events
  • KIE Live
    Worldwide - Tue 19 January 2021
    • OptaPlanner Shadow Variables for the Vehicle Routing Problem and Task Assignment by Geoffrey De Smet, Karina Varela, Alex Porcelli
  • Javaland
    Worldwide - Tue 16 March 2021
    • AI on Quarkus: I love it when an OptaPlan comes together by Geoffrey De Smet
Add event / Archive
Latest blog posts
  • Solve the facility location problem
    Fri 9 October 2020
     Jiří Locker
  • OptaPlanner Week 2020 recordings
    Mon 7 September 2020
     Geoffrey De Smet
  • Let’s OptaPlan your jBPM tasks (part 1) - Integrating the two worlds
    Fri 3 July 2020
     Walter Medvedeo
  • AI versus Covid-19: How Java helps nurses and doctors in this fight
    Fri 8 May 2020
     Christopher Chianelli
  • Workflow processes with AI scheduling
    Tue 5 May 2020
     Christopher Chianelli
  • Constraint Streams - Modern Java constraints without the Drools Rule Language
    Tue 7 April 2020
     Geoffrey De Smet
  • How to plan (and optimize) a Secret Santa
    Wed 18 December 2019
     Christopher Chianelli
Blog archive
Latest videos
  • YT Shadow variables
    Tue 19 January 2021
     Geoffrey De Smet
  • YT Domain modeling and design patterns
    Tue 17 November 2020
     Geoffrey De Smet
  • YT Quarkus insights: AI constraint solving
    Tue 20 October 2020
     Geoffrey De Smet
  • YT AI in kotlin
    Wed 23 September 2020
     Geoffrey De Smet
  • YT Planning agility: continuous planning, real-time planning and more
    Thu 3 September 2020
     Geoffrey De Smet
  • YT Quarkus and OptaPlanner: create a school timetable application
    Thu 3 September 2020
     Radovan Synek
  • YT Business use cases and the impact of OptaPlanner
    Thu 3 September 2020
     Satish Kale
Video archive

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  • Drools rule engine
  • OptaPlanner constraint solver
  • jBPM workflow engine

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