We are happy to announce the 6.4 Final release of OptaPlanner. OptaPlanner is a lightweight, embeddable planning engine written in Java™ to solve constraint satisfaction problems efficiently. It is released alongside the Drools rule engine and the jBPM workflow engine.
Each of these videos demonstrates an example and/or a special feature:
Conference scheduling: Integration with Excel
Employee rostering: Web application in the cloud
Hospital bed planning: Overconstrained planning
Task assigning: Real-time and continuous planning
Meeting scheduling: Time grain design pattern
Exam timetabling: User defined score parametrization
Dinner party scheduling: Decision tables
Cloud optimization: Decision tables
Cloud optimization: Real time planning
Course scheduling: Immovable planning entities
Project job scheduling: Built-in hard constraints
Tennis club scheduling: Fairness and load balancing constraints
Vehicle routing with time windows: Shadow variables and real-time planning
Vehicle routing scoring: Score function flexibility
Employee rostering: Continuous planning
The RHDM version does NOT match the OptaPlanner version:
| RHDM version | OptaPlanner version |
|---|---|
| 7.2 | 7.14 |
| 7.3 | 7.18 |
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 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:
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>When Nearby Selection is used in Local Search, it’s memory consumption is heavily reduced, especially when scaling out:
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.
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>Assign meetings of different durations to starting times and rooms.
Also see the blog about time scheduling design patterns.
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.
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).
The best and easiest way to upgrade to this new version of OptaPlanner is by following the upgrade recipe.
Read the previous release notes to learn about the new and noteworthy in previous releases.
Radovan Synek
Musa Talluzi
Geoffrey De Smet
Musa Talluzi
Radovan Synek
Sudheer Chekka
Musa Talluzi
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