We are happy to announce the 6.2 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 |
| 7.4 | 7.23 |
| 7.5 | 7.26 |
| 7.6 | 7.30 |
Nearby selection allows a vehicle routing problem to scale out gracefully beyond 1000 locations, without the need for partitioning. It works by focusing on move selections that modify locations that are near each other:
It results in much better scalability on larger datasets, for example a VRP with 2750 customers
(higher is better), which reduces fuel and labor expenses by 15% in a 5 minute run:
Several nearby selection probability distributions are supported: block distribution, linear distribution, parabolic distribution and beta distribution.
TailChainSwapMove is a new move type for chained variables. It’s a subset of SubchainChangeMove and SubchainSwapMove, but it’s generally more efficient, especially for time windowed cases.
In our benchmarks, a union of ChangeMove, SwapMove and TailChainSwapMove (using nearby selection on all 3) performed best.
VRP with a @InverseRelationShadowVariable is now more efficient. In some cases, it’s up to 32% faster.
Strategic Oscillation Tabu Search is often an improvement over normal Tabu Search. Instead of picking the accepted move with the highest score, it employs a different mechanism: If there’s an improving move, it picks it. If there’s no improving move however, it prefers moves which improve a softer score level, over moves which break a harder score level less.
To enable it, do this:
<localSearch>
...
<acceptor>
<entityTabuSize>7</entityTabuSize>
</acceptor>
<forager>
<acceptedCountLimit>1000</acceptedCountLimit>
<finalistPodiumType>STRATEGIC_OSCILLATION</finalistPodiumType>
</forager>
</localSearch>Schedule all tasks in time and on a machine to minimize the power cost. Each machine must have enough hardware to run all of its tasks. Each task and machine consumes power. The power price differs over time.
Based on contributions by Lukáš Petrovický.
These new statistics visualize how the individual constraint types change over time.
This gives a better insight as to which constraints impact the score the most.
Construction Heuristics: new pick early type: FIRST_FEASIBLE_SCORE which is useful for scaling.
Benchmarker: logarithmic scale for Problem scale axis when appropriate. Contributed by Ondrej Skopek.
BendableLongScore: Bendable score with long types. Contributed by Dieter De Paepe.
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.
Julian Cui
Radovan Synek
Musa Talluzi
Geoffrey De Smet
Musa Talluzi
Radovan Synek
Sudheer Chekka
Comments
Visit our forum to comment