We are happy to announce the 6.1 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:
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: Build-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 BRMS and BPM Suite version does NOT match the OptaPlanner version:
| BRMS / BPM Suite version | OptaPlanner version |
|---|---|
| 6.0 | 6.0 |
| 6.1 | 6.2 |
| 6.2 | 6.3 |
| 6.3 | 6.4 |
| 6.4 | 6.5 |
There is now a public API:
Public API: All classes in the package namespace org.optaplanner.core.api are 100% backwards compatible in future releases.
Impl classes: All classes in the package namespace org.optaplanner.core.impl are not backwards compatible: they might change in future releases.
The recipe called UpgradeFromPreviousVersionRecipe.txt describes every such change and on how to quickly deal with it
when upgrading to a newer version. That recipe file is included in every release zip.
XML configuration: The XML solver configuration is backwards compatible for all elements,
except for elements that require the use of non public API classes.
The XML solver configuration is defined by the classes in the package namespace org.optaplanner.core.config.
EntitySelector caching speedup for large datasets: Benchmarked on MachineReassignment’s B datasets.
Tabu Search: +3% on average, +4% on largest
Late Acceptance: +234% (x3.34) on average, +430% (x5.3) on largest
Generic cloning speedup for large datasets: Benchmarked on MachineReassignment’s B datasets.
Tabu Search: +18% on average, +24% on largest
Late Acceptance: +23% on average, +121% (x2.21) on largest
When the planning value range of a planning variable is a number (such as int, double or BigDecimal),
you can now declare the range by its bounds, instead of enumerating all values in the range.
So instead of reserving 5000 memory spaces for all the integer values:
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@ValueRangeProvider(id = "delayRange")
public List<Integer> getDelayRange() {
List<Integer> = new ArrayList<Integer>(5000);
for (int i = 0; i < 5000; i++) {
delayRange.add(i);
}
return delayRange;
}You can now just reserve 2 memory spaces for the bounds of those integer values:
@ValueRangeProvider(id = "delayRange")
public CountableValueRange<Integer> getDelayRange() {
return ValueRangeFactory.createIntValueRange(0, 5000);
}A new Construction Heuristic that takes considerably longer than First Fit, First Fit Decreasing, but often has better results.
<constructionHeuristic>
<constructionHeuristicType>CHEAPEST_INSERTION</constructionHeuristicType>
</constructionHeuristic>Branch And Bound is a smart version of Brute Force. It also explores nodes in an exponential search tree, but it investigates more promising nodes first and prunes away worthless nodes. Configure it like this:
<exhaustiveSearch>
<exhaustiveSearchType>BRANCH_AND_BOUND</exhaustiveSearchType>
</exhaustiveSearch>The BenchmarkAggregator in optaplanner-benchmark allows you to merge multiple benchmark reports into 1 report.
Contributed by Matej Čimbora.
New PillarChangeMove:
A pillar is a set of planning entities which have the same planning value(s) for their planning variable(s).
The PillarChangeMove selects 1 entity pillar and changes the value of 1 variable for all entities.
The pillar moves can now select subPillars too.
The Manners2009 example has been cleaned up and renamed to the Dinner Party example.
Decision Table example: the Dinner Party example now demonstrates how to use a Drools Decision Table (an XLS file) to allow users to define score rules.
New BestScoreFeasibleTermination: terminates when the best score is feasible. Contributed by Matej Čimbora.
Real-time planning improvements, such as a daemon mode.
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.
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