Release Notes 6.1

We are happy to announce a 6.1 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
9.44.0.Final

Documentation
9.44.0.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
7.8	7.39
7.9	7.44
7.1	7.48
7.11	8.5 (and 7.52)
7.12	8.11 (and 7.59)
7.13	8.13 (and 7.67)

New and noteworthy

Public API

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.

Performance speedups

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

Declarative value ranges

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:

    @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);
    }

New Construction Heuristic: Cheapest Insertion

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>

New Exhaustive Search: Branch And Bound

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>

BenchmarkAggregator

The BenchmarkAggregator in optaplanner-benchmark allows you to merge multiple benchmark reports into 1 report.

Contributed by Matej Čimbora.

Generic move improvements

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.

Other improvements

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.