We are happy to announce the 6.0 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 |
OptaPlanner is the new name for Drools Planner. For more information, see the full announcement.
The new constraint match system is:
Faster: The average calculate count per seconds increases between 7% and 40% on average per use case.
Easier to read and write
Less error-prone: It’s much harder to cause score corruption in your DRL.
Before:
rule "conflictingLecturesSameCourseInSamePeriod"
when
...
then
insertLogical(new IntConstraintOccurrence("conflictingLecturesSameCourseInSamePeriod", ConstraintType.HARD,
-1,
$leftLecture, $rightLecture));
endAfter:
rule "conflictingLecturesSameCourseInSamePeriod"
when
...
then
scoreHolder.addHardConstraintMatch(kcontext, -1);
endNotice that you don’t need to repeat the rule name or the causes (the lectures) no more.
OptaPlanner figures out it itself through the kcontext variable.
Drools automatically exposes the kcontext variable in the RHS, so you don’t need any extra code for it.
You also no longer need to hack the API’s to get a list of all ConstraintOcurrence’s:
the `ConstraintMatch objects (and their totals per constraint) are available directly on the ScoreDirector API.
For more information, see this blog post and the reference manual.
The construction heuristics now use the selector architecture so they support selection filtering, etc. Sorting can be overwritten at a configuration level (very handy for benchmarking).
In use cases with multiple planning variables (for example period, room and teacher), you can now switch to a far more scalable configuration.
Implementing the `Solution’s planning clone method is now optional. This means there’s less boilerplate code to write and it’s harder to cause score corruption in your code.
For more information, see this blog post and the reference manual.
There are new built-in score definitions, such as: HardMediumSoftScore (3 score levels),
BendableScore (configurable number of score levels), HardSoftLongScore, HardSoftDoubleScore,
HardSoftBigDecimalScore, …
A planning variable can now have a listener which updates a shadow planning variable.
A shadow planning variable is a variable that is never changed directly,
but can be calculated based on the state of the genuine planning variables.
For example: in VRP with time windows, the arrivalTime at a customer can be calculated
based on the previous customers of that vehicle.
A shadow planning variable can now be mappedBy a genuine variable.
OptaPlanner will automatically update the shadow variable if the genuine variable is changed.
Currently this is only supported for chained variables.
Several improvements have made use cases like TSP and VRP more scalable.
The code has been optimized to run significantly faster.
Also, <subChainSelector> now supports <maximumSubChainSize> to scale out better.
Step Counting Hill Climbing is easy to configure and has good results. Generally, it behaves and feels a lot like Late Acceptance. Try it out with the Benchmarker.
Best solution mutation statistic: shows for every new best solution found, how many variables needed to change to improve the last best solution.
Step score statistic: shows how the step score evolves over time.
The Benchmarker now highlights infeasible solutions with an orange exclamation mark.
The Benchmarker now shows standard deviation per solver configuration (thanks to Miroslav Svitok).
VRP with time windows: The VRP example can now also handle the capacitated vehicle routing problem with time windows.
Project job scheduling: A form of job shop scheduling, for example to schedule the production of diverse books, cars or other products to machines and employees (thanks to Lukáš Petrovický)
The GUI’s of course scheduling, exam scheduling, hospital bed planning and sport scheduling have been improved.
The optaplanner examples Swing GUI has been redesigned to take up less space.
A webapp variant of the cloud balancing example has been added to optaplanner-webexamples.war (thanks to Frederic Hornain).
Domain classes that extend/implement a @PlanningEntity class or interface can now be used as planning entities.
Nullable variables support improved and fixed.
Late Acceptance improved.
Ratio based entity tabu (thanks to Lukáš Petrovický).
Drools properties can now be optionally specified in the solver configuration XML.
Mimic selection: useful to create a cartesian product selection of 2 change move selectors that move different variables of the same entity.
KieBase support, for example to integrate decision tables or scorecards.
OSGi support out-of-the-box in the optaplanner jars.
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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