Constraint Streams get some more love


We have recently merged a lot of improvements to OptaPlanner‘s Constraint Streams API
in order to make it faster and even easier to use.
Let’s take a closer look at some of them.

Constraint Streams by default

I find it hard to believe that it’s been over two years already since we’ve introduced Constraint Streams into OptaPlanner; how time flies!

To get you started, OptaPlanner has always brought a wide selection of examples and more recently quickstarts.
While quickstarts have always used Constraint Streams and Constraint Streams only, the examples being older in age, they typically bring more scoring implementations, such as DRL or incremental Java.
To clearly state that Constraint Streams are now the scoring method of choice, we have recently converted all OptaPlanner examples to use Constraint Streams by default.
That said, we do not intend to deprecate any of the other scoring methods any time soon.

New constraint collectors

Seeing increased adoption of Constraint Streams, we have been expanding the selection of constraint collectors available out of the box.
Recently, we have added the following new constraint collectors:

  • average() constraint collector allows you to calculate an average of a group of items.
  • compose() collector allows you to merge results of several constraint collectors. (For example, the average() collector is a composite of count() and sum().)
  • conditionally() constraint collectors allows you to only delegate to another collector if a given condition is met first.

Especially with the latter two collectors, the expressive power of Constraint Streams has grown significantly.

Faster constraint collectors

Until recently, constraint collectors toList(), toSet(), toSortedSet(), toMap() and toSortedMap() have been comparatively slow.
We have now changed the underlying implementation to be much more friendly to incremental calculation, and the end result is a performance improvement on the order of magnitudes on large enough data sets.

Experimental constraint collectors

As we were implementing constraint providers for all the various examples, we noticed some constraints (which were already hard to implement in DRL) were impossible with Constraint Streams as it stands.
Consider the following Nurse Rostering DRL-based constraint to penalize too many consecutive shifts:

    rule "insertEmployeeConsecutiveAssignmentStart" when
        ... // Omitted for brevity.
        insertLogical(new EmployeeConsecutiveAssignmentStart($employee, $shiftDate));
    rule "insertEmployeeConsecutiveAssignmentEnd" when
        ... // Omitted for brevity.
        insertLogical(new EmployeeConsecutiveAssignmentEnd($employee, $shiftDate));
    rule "insertEmployeeWorkSequence" when
        EmployeeConsecutiveAssignmentStart($employee : employee, $firstDayIndex : shiftDateDayIndex)
        EmployeeConsecutiveAssignmentEnd(employee == $employee, shiftDateDayIndex >= $firstDayIndex, $lastDayIndex : shiftDateDayIndex )
        not EmployeeConsecutiveAssignmentEnd(employee == $employee, shiftDateDayIndex >= $firstDayIndex && < $lastDayIndex)
        insertLogical(new EmployeeWorkSequence($employee, $firstDayIndex, $lastDayIndex));
    rule "minimumConsecutiveWorkingDays" when
        $contractLine : MinMaxContractLine(
            contractLineType == ContractLineType.CONSECUTIVE_WORKING_DAYS, minimumEnabled == true,
            $contract : contract, $minimumValue : minimumValue
        EmployeeWorkSequence(getEmployee().getContract() == $contract, dayLength < $minimumValue, $dayLength : dayLength)
        scoreHolder.addSoftConstraintMatch(kcontext, ($dayLength - $minimumValue) * $contractLine.getMinimumWeight());

As you can see, this is a lot of DRL which fundamentally does this:

  1. Infer the first shift in a sequence of consecutive shifts.
  2. Infer the last shift in a sequence of consecutive shifts.
  3. Infer all the non-overlapping sequences.
  4. Penalize sequences longer than what is prescribed by the contract.

Now consider how the same constraint is accomplished with Constraint Streams, using the experimental consecutive constraint collector:

    Constraint consecutiveWorkingDays(ConstraintFactory constraintFactory) {
        return constraintFactory.from(MinMaxContractLine.class)
            .filter(minMaxContractLine -> minMaxContractLine
                .getContractLineType() == ContractLineType.CONSECUTIVE_WORKING_DAYS &&
                Joiners.equal(ContractLine::getContract, ShiftAssignment::getContract))
            .groupBy((contract, shift) -> shift.getEmployee(),
                (contract, shift) -> contract,
                ExperimentalConstraintCollectors.consecutive((contract, shift) -> shift.getShiftDate(),
            .filter((employee, contract, shiftList) -> contract.isViolated(shiftList.getLength()))
            .penalize("consecutiveWorkingDays", HardSoftScore.ONE_SOFT,
                    (employee, contract, shiftList) -> contract.getViolationAmount(shiftList.getLength()));

This constraint uses the experimental consecutive constraint collector to get us a list of all sequences of consecutive shifts.
This list is then flattened, giving us each sequence individually.
As you can see, this is a much more concise implementation of the same behavior, with the brunt of the logic hidden inside the constraint collector itself.

This constraint collector is not part of our public API and we consider it experimental.
Before we make it part of the public API, we are looking for your feedback to make sure it fits the various use cases that are out there.
If you have any questions or see issues in applying this pattern to your own constraints, do not hesitate to reach out to us.


All of these improvements are available as of OptaPlanner 8.11.0.Final, coming soon to a mirror near you.

We will continue improving Constraint Streams as we find more and more problems to solve.
If you want to make sure we can solve your problems too, share them with us.


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