Different solvers solve problems in different ways. Each type has advantages and disadvantages. We 'll roughly discuss a few of the solver types here. You can safely skip this section.
Brute force creates and evaluates every possible solution, usually by creating a search tree.
Advantages:
It finds an optimal solution. If there is more then 1 optimal solution, it finds all optimal solutions.
It is straightforward and simple to implement.
Disadvantages:
It has a horrible performance and scalability. Mostly unusable for a real-world problem due to time constraints.
Brute force is currently not implemented in drools-solver. But we have plans to implement it in the future, as a reference for validating the output of the other solver types.
Branch and bound is an improvement over brute force, as it prunes away subsets of solutions which cannot have a better solution than the best solution already found at that point.
Advantages:
It finds an optimal solution. If there is more then 1 optimal solution, it can find all optimal solutions if needed.
Disadvantages:
It still scales very badly.
Branch and bound is currently not implemented in drools-solver.
Simplex turns all constraints and data into a big equation, which it transmutes into a mathematical function without local optima. It then finds an optimal solution to the planning problem by finding an optima of that mathematical function.
Advantages:
It finds an optimal solution.
Disadvantages:
It's usually rather complex and mathematical to implement constraints.
Drools-solver does not currently implement simplex.
Local search starts from an initial solution and evolves that single solution into a better and better solution. It uses a single search path of solutions. At each solution in this path it evaluates a number of possible moves on the solution and applies the most suitable move to take the step to the next solution.
Local search works a lot like a human planner: it uses a single search path and moves facts around to find a good feasible solution.
A simple local search can easily get stuck in a local optima, but improvements (such as tabu search and simulated annealing) address this problem.
Advantages:
It's relatively simple and natural to implement constraints (at least in drools-solver's implementation).
It's very scalable, even when adding extra constraints (at least in drools-solver's implementation).
It generally needs to worry about less negative hard constraints, because the move pattern can fulfill a number of the negative hard constraints.
Disadvantages:
It does not know when it has found an optimal solution.
If the optimal score is unknown (which is usually the case), it must be told when to stop looking (for example based on time spend, user input, ...).
Drools-solver implements local search, including tabu search and simulated annealing.
Every build-in solver implemented in drools-solver implements the Solver
interface:
public interface Solver { void setStartingSolution(Solution solution); Score getBestScore(); Solution getBestSolution(); void solve(); // ... }
Solving a planning problem with drools-solver consists out of 4 steps:
Build a solver, for example a tabu search solver for any NQueens puzzle.
Set a starting solution on the solver, for example a 4 Queens puzzle instance.
Solve it.
Get the best solution found by the solver.
A Solver
should currently directly be accessed from a single thread. Support from accessing
it from a different thread, for example to finish solving early or to change the problem facts in real-time, will be
added in future releases.
You can build a Solver
instance with the XmlSolverConfigurer
. Configure
it with a solver configuration xml file:
XmlSolverConfigurer configurer = new XmlSolverConfigurer(); configurer.configure("/org/drools/solver/examples/nqueens/solver/nqueensSolverConfig.xml"); Solver solver = configurer.buildSolver();
A basic solver configuration file looks something like this:
<?xml version="1.0" encoding="UTF-8"?> <localSearchSolver> <scoreDrl>/org/drools/solver/examples/nqueens/solver/nQueensScoreRules.drl</scoreDrl> <scoreDefinition> <scoreDefinitionType>SIMPLE</scoreDefinitionType> </scoreDefinition> <finish> <feasableScore>0</feasableScore> </finish> <selector> <moveFactoryClass>org.drools.solver.examples.nqueens.solver.NQueensMoveFactory</moveFactoryClass> </selector> <accepter> <completeSolutionTabuSize>1000</completeSolutionTabuSize> </accepter> <forager> <foragerType>MAX_SCORE_OF_ALL</foragerType> </forager> </localSearchSolver>
This is a tabu search configuration for n queens. We 'll explain the various parts of a configuration later in this manual.
Drools-solver makes it relatively easy to switch a solver type just by changing the configuration. There's even a benchmark utility which allows you to play out different configurations against each other and report the most appropriate configuration for your problem. You could for example play out tabu search versus simulated annealing, on 4 queens and 64 queens.
A solver has a single Random
instance. Some solver configurations use that instance a lot
more than others. For example simulated annealing depends highly on random numbers, while tabu search only depends
on it to deal with score ties. In any case, during your testing it's advisable to set that Random
instance, so your tests are reproducible.
A Solver can only solve 1 problem at a time.
You need to present the problem as a starting Solution
instance to the solver.
You need to implement the Solution
interface:
public interface Solution { Collection<? extends Object> getFacts(); Solution cloneSolution(); }
For example, an NQueens instance just holds a list of all it's queens:
public class NQueens implements Solution { private List<Queen> queenList; // ... }
All Objects returned by the getFacts()
method will be asserted into the drools working
memory. Those facts can be used by the score rules. For example, NQueens
just returns all
Queen
instances.
public Collection<? extends Object> getFacts() { return queenList; }
Most solvers use the cloneSolution()
method to clone the solution each time they
encounter a new best solution. The NQueens
implementation just clones all
Queen
instances:
public NQueens cloneSolution() { NQueens clone = new NQueens(); List<Queen> clonedQueenList = new ArrayList<Queen>(queenList.size()); for (Queen queen : queenList) { clonedQueenList.add(queen.clone()); } clone.queenList = clonedQueenList; return clone; }
The cloneSolution()
method should clone no more and no less than the parts of the
Solution
that can change during solving. For example, in the lesson schedule example the
lessons are cloned, but teachers, groups and timeslots are not cloned because only a lesson's appointed timeslot
changes during solving:
/** * Clone will only deep copy the lessons */ public LessonSchedule cloneSolution() { LessonSchedule clone = new LessonSchedule(); clone.timeslotList = timeslotList; // No Deep copy clone.teacherList = teacherList; // No Deep copy clone.groupList = groupList; // No Deep copy List<Lesson> clonedLessonList = new ArrayList<Lesson>(lessonList.size()); for (Lesson lesson : lessonList) { clonedLessonList.add(lesson.clone()); } clone.lessonList = clonedLessonList; return clone; }
First, you will need to make a starting solution and set that on the solver:
solver.setStartingSolution(startingSolution);
For 4 queens we use a simple filler algorithm that creates a starting solution with all queens on a different x and on the same y (with y = 0).
Here's how we generate it:
private NQueens createNQueens(int n) { NQueens nQueens = new NQueens(); nQueens.setId(0L); List<Queen> queenList = new ArrayList<Queen>(n); for (int i = 0; i < n; i++) { Queen queen = new Queen(); queen.setId((long) i); queen.setX(i); // Different column queen.setY(0); // Same row queenList.add(queen); } nQueens.setQueenList(queenList); return nQueens; }
The starting solution will probably be far from optimal (or even feasible). Here, it's actually the worst possible solution. However, we 'll let the solver find a much better solution for us anyway.
For large problems, a simple filler algorithm like createNQueens(int)
doesn't suffice. A
(local search) solver starting from a bad starting solution wastes a lot of time to reach a solution which an
initializer algorithm can generate in a fraction of that time.
An initializer algorithm ussually works something like this:
It sorts the unplanned elements in a queue according to some general rules, for example by exam student size.
Next, it plans them in the order they come from the queue. Each element is put the best, still available spot.
It doesn't change an already planned element. It exits when the queue is empty and all elements are planned.
Such an algorithm is very deterministic: it's really fast, but you can't give it more time to generate an
even better solution. In some cases the solution it generates will be feasible, but in most cases it won't. You
'll need a real solver to get to a feasible or more optimal solution. Nevertheless you 'll want to such an
initializer to give the real solver a serious head start. You can do this by implementing the
StartingSolutionInitializer
interface:
public interface StartingSolutionInitializer extends SolverAware { boolean isSolutionInitialized(Solution solution); void initializeSolution(Solution solution); }
You'll need to set a (uninitialized) solution on the solver. Once the solver starts, it will first call the
StartingSolutionInitializer
to initialize the solution. If the
StartingSolutionInitializer
adds, edits or removes facts it needs to notify the workingMemory
about this. It can use score calculation during its intialization process.
Here's an example on how you add the StartingSolutionInitializer
to the
configuration:
<localSearchSolver> ... <startingSolutionInitializerClass>org.drools.solver.examples.itc2007.examination.solver.solution.initializer.ExaminationStartingSolutionInitializer</startingSolutionInitializerClass> ... </localSearchSolver>
Solving a problem is quite easy once you have a solver and the starting solution:
solver.setStartingSolution(startingSolution); solver.solve(); Solution bestSolution = solver.getBestSolution();
The solve()
method will take a long time (depending on the problem size and the solver
configuration). The solver will remember (actually clone) the best solution it encounters during its solving.
Depending on a number factors (including problem size, how long you allow the solver to work, which solver type you
use, ...), that best solution will be a feasible or even an optimal solution.
After a problem is solved, you can reuse the same solver instance to solve another problem (of the same problem type).