Examples with Placement org.cpsolver.coursett.model.Placement used on opensource projects

Example 21 with Placement

use of org.cpsolver.coursett.model.Placement in project cpsolver by UniTime.

the class LectureSelection method selectVariable.

@Override
public Lecture selectVariable(Solution<Lecture, Placement> solution) {
    TimetableModel model = (TimetableModel) solution.getModel();
    Assignment<Lecture, Placement> assignment = solution.getAssignment();
    Collection<Lecture> unassignedVariables = model.unassignedVariables(assignment);
    if (iInteractiveMode) {
        // remove variables that have no values
        unassignedVariables = new ArrayList<Lecture>(unassignedVariables.size());
        for (Lecture variable : model.unassignedVariables(assignment)) {
            if (!variable.values(solution.getAssignment()).isEmpty())
                unassignedVariables.add(variable);
        }
    }
    if (unassignedVariables.isEmpty()) {
        Collection<Lecture> variables = model.perturbVariables(assignment);
        if (variables.isEmpty())
            variables = model.assignedVariables(assignment);
        if (iRW && ToolBox.random() <= iRandomWalkProb)
            return ToolBox.random(variables);
        List<Lecture> selectionVariables = new ArrayList<Lecture>();
        double worstValue = 0.0;
        for (Iterator<Lecture> i1 = (iSubSetSelection ? ToolBox.subSet(variables, iSelectionSubSetPart, iSelectionSubSetMinSize) : variables).iterator(); i1.hasNext(); ) {
            Lecture selectedVariable = i1.next();
            if (iTabu != null && iTabu.contains(selectedVariable))
                continue;
            double value = assignment.getValue(selectedVariable).toDouble(assignment);
            if (selectionVariables.isEmpty() || value > worstValue) {
                selectionVariables.clear();
                selectionVariables.add(selectedVariable);
                worstValue = value;
            } else if (worstValue == value) {
                selectionVariables.add(selectedVariable);
            }
        }
        Lecture selectedVariable = ToolBox.random(selectionVariables);
        if (selectedVariable == null)
            selectedVariable = ToolBox.random(variables);
        if (selectedVariable != null && iTabu != null) {
            if (iTabu.size() == iTabuPos)
                iTabu.add(selectedVariable);
            else
                iTabu.set(iTabuPos, selectedVariable);
            iTabuPos = (iTabuPos + 1) % iTabuSize;
        }
        return selectedVariable;
    } else {
        if (ToolBox.random() <= iRandomWalkProb)
            return ToolBox.random(unassignedVariables);
        if (iProp != null && iUnassignWhenNotGood) {
            List<Lecture> noGoodVariables = new ArrayList<Lecture>();
            for (Iterator<Lecture> i1 = ToolBox.subSet(unassignedVariables, iSelectionSubSetPart, iSelectionSubSetMinSize).iterator(); i1.hasNext(); ) {
                Lecture variable = i1.next();
                if (iProp.goodValues(assignment, variable).isEmpty())
                    noGoodVariables.add(variable);
            }
            if (!noGoodVariables.isEmpty()) {
                if (ToolBox.random() < 0.02)
                    return ToolBox.random(assignment.assignedVariables());
                for (int attempt = 0; attempt < 10; attempt++) {
                    Lecture noGoodVariable = ToolBox.random(noGoodVariables);
                    Placement noGoodValue = ToolBox.random(noGoodVariable.values(solution.getAssignment()));
                    if (!iProp.noGood(assignment, noGoodValue).isEmpty())
                        return ToolBox.random(iProp.noGood(assignment, noGoodValue)).variable();
                }
            }
        }
        if (iRouletteWheelSelection) {
            int iMaxDomainSize = 0;
            int iMaxGoodDomainSize = 0;
            int iMaxConstraints = 0;
            Collection<Lecture> variables = (iSubSetSelection ? ToolBox.subSet(unassignedVariables, iSelectionSubSetPart, iSelectionSubSetMinSize) : unassignedVariables);
            for (Lecture variable : variables) {
                if (iTabu != null && iTabu.contains(variable))
                    continue;
                iMaxDomainSize = Math.max(iMaxDomainSize, variable.values(solution.getAssignment()).size());
                iMaxGoodDomainSize = (iProp == null ? 0 : Math.max(iMaxGoodDomainSize, iProp.goodValues(assignment, variable).size()));
                iMaxConstraints = Math.max(iMaxConstraints, variable.constraints().size());
            }
            List<Integer> points = new ArrayList<Integer>();
            int totalPoints = 0;
            for (Lecture variable : variables) {
                long pointsThisVariable = Math.round(iDomainSizeWeight * (((double) (iMaxDomainSize - variable.values(solution.getAssignment()).size())) / ((double) iMaxDomainSize)) + (iProp == null ? 0.0 : iGoodValuesWeight * (((double) (iMaxGoodDomainSize - iProp.goodValues(assignment, variable).size())) / ((double) iMaxGoodDomainSize))) + iConstraintsWeight * (((double) (iMaxConstraints - variable.constraints().size())) / ((double) iMaxConstraints)) + iInitialAssignmentWeight * (variable.getInitialAssignment() != null ? model.conflictValues(assignment, variable.getInitialAssignment()).size() : 0.0));
                if (pointsThisVariable > 0) {
                    totalPoints += pointsThisVariable;
                    points.add(totalPoints);
                }
            }
            if (totalPoints > 0) {
                int rndPoints = ToolBox.random(totalPoints);
                Iterator<Lecture> x = variables.iterator();
                for (int i = 0; x.hasNext() && i < points.size(); i++) {
                    Lecture variable = x.next();
                    int tp = points.get(i);
                    if (tp > rndPoints) {
                        if (variable != null && iTabu != null) {
                            if (iTabu.size() == iTabuPos)
                                iTabu.add(variable);
                            else
                                iTabu.set(iTabuPos, variable);
                            iTabuPos = (iTabuPos + 1) % iTabuSize;
                        }
                        return variable;
                    }
                }
            }
        } else {
            List<Lecture> selectionVariables = null;
            long bestGood = 0;
            for (Iterator<Lecture> i = ToolBox.subSet(unassignedVariables, iSelectionSubSetPart, iSelectionSubSetMinSize).iterator(); i.hasNext(); ) {
                Lecture variable = i.next();
                if (iTabu != null && iTabu.contains(variable))
                    continue;
                long good = (long) (iDomainSizeWeight * variable.values(solution.getAssignment()).size() + iGoodValuesWeight * (iProp == null ? 0 : iProp.goodValues(assignment, variable).size()) + iConstraintsWeight * variable.constraints().size() + iInitialAssignmentWeight * (variable.getInitialAssignment() != null ? model.conflictValues(assignment, variable.getInitialAssignment()).size() : 0.0));
                if (selectionVariables == null || bestGood > good) {
                    if (selectionVariables == null)
                        selectionVariables = new ArrayList<Lecture>();
                    else
                        selectionVariables.clear();
                    bestGood = good;
                    selectionVariables.add(variable);
                } else if (good == bestGood) {
                    selectionVariables.add(variable);
                }
            }
            if (!selectionVariables.isEmpty()) {
                Lecture selectedVariable = ToolBox.random(selectionVariables);
                if (selectedVariable != null && iTabu != null) {
                    if (iTabu.size() == iTabuPos)
                        iTabu.add(selectedVariable);
                    else
                        iTabu.set(iTabuPos, selectedVariable);
                    iTabuPos = (iTabuPos + 1) % iTabuSize;
                }
                return selectedVariable;
            }
        }
        Lecture selectedVariable = ToolBox.random(unassignedVariables);
        if (selectedVariable != null && iTabu != null) {
            if (iTabu.size() == iTabuPos)
                iTabu.add(selectedVariable);
            else
                iTabu.set(iTabuPos, selectedVariable);
            iTabuPos = (iTabuPos + 1) % iTabuSize;
        }
        return selectedVariable;
    }
}

Example 22 with Placement

use of org.cpsolver.coursett.model.Placement in project cpsolver by UniTime.

the class NeighbourSelectionWithSuggestions method backtrack.

private void backtrack(NeighbourSelectionWithSuggestionsContext context, List<Lecture> initialLectures, Map<Lecture, Placement> resolvedLectures, HashMap<Lecture, Placement> conflictsToResolve, int depth) {
    int nrUnassigned = conflictsToResolve.size();
    if ((initialLectures == null || initialLectures.isEmpty()) && nrUnassigned == 0) {
        context.setSuggestionNeighbourIfImproving(resolvedLectures);
        return;
    }
    if (depth <= 0 || context.checkTimeoutReached())
        return;
    Assignment<Lecture, Placement> assignment = context.getAssignment();
    for (Lecture lecture : initialLectures != null && !initialLectures.isEmpty() ? initialLectures : new ArrayList<Lecture>(conflictsToResolve.keySet())) {
        if (context.isTimeoutReached())
            break;
        if (resolvedLectures.containsKey(lecture))
            continue;
        placements: for (Placement placement : lecture.values(assignment)) {
            if (context.isTimeoutReached())
                break;
            Placement cur = assignment.getValue(lecture);
            if (placement.equals(cur))
                continue;
            if (placement.isHard(assignment))
                continue;
            Set<Placement> conflicts = context.getModel().conflictValues(assignment, placement);
            if (nrUnassigned + conflicts.size() > depth)
                continue;
            if (conflicts.contains(placement))
                continue;
            if (containsCommited(context, conflicts))
                continue;
            for (Iterator<Placement> i = conflicts.iterator(); i.hasNext(); ) {
                Placement c = i.next();
                if (resolvedLectures.containsKey(c.variable()))
                    continue placements;
            }
            for (Iterator<Placement> i = conflicts.iterator(); i.hasNext(); ) {
                Placement c = i.next();
                assignment.unassign(0, c.variable());
            }
            assignment.assign(0, placement);
            for (Iterator<Placement> i = conflicts.iterator(); i.hasNext(); ) {
                Placement c = i.next();
                conflictsToResolve.put(c.variable(), c);
            }
            Placement resolvedConf = conflictsToResolve.remove(lecture);
            resolvedLectures.put(lecture, placement);
            backtrack(context, null, resolvedLectures, conflictsToResolve, depth - 1);
            resolvedLectures.remove(lecture);
            if (cur == null)
                assignment.unassign(0, lecture);
            else
                assignment.assign(0, cur);
            for (Iterator<Placement> i = conflicts.iterator(); i.hasNext(); ) {
                Placement p = i.next();
                assignment.assign(0, p);
                conflictsToResolve.remove(p.variable());
            }
            if (resolvedConf != null)
                conflictsToResolve.put(lecture, resolvedConf);
        }
    }
}

Example 23 with Placement

use of org.cpsolver.coursett.model.Placement in project cpsolver by UniTime.

the class MinimizeNumberOfUsedRoomsConstraint method computeConflicts.

@Override
public void computeConflicts(Assignment<Lecture, Placement> assignment, Placement placement, Set<Placement> conflicts) {
    MinimizeNumberOfUsedRoomsConstraintContext context = getContext(assignment);
    int overLimit = context.getOverLimit(assignment, placement);
    if (overLimit > 0) {
        List<List<Placement>> adepts = new ArrayList<List<Placement>>();
        for (Set<Lecture> lects : context.getUsedRooms().values()) {
            List<Placement> placementsToUnassign = new ArrayList<Placement>();
            boolean canUnassign = true;
            for (Lecture l : lects) {
                if (l.isCommitted()) {
                    canUnassign = false;
                    break;
                }
                Placement p = assignment.getValue(l);
                if (!conflicts.contains(p))
                    placementsToUnassign.add(p);
            }
            if (!canUnassign)
                continue;
            adepts.add(placementsToUnassign);
        }
        if (adepts.size() < overLimit) {
            conflicts.add(placement);
        } else {
            Collections.sort(adepts, new Comparator<List<Placement>>() {

                @Override
                public int compare(List<Placement> c1, List<Placement> c2) {
                    return Double.compare(c1.size(), c2.size());
                }
            });
            for (int i = 0; i < overLimit; i++) {
                conflicts.addAll(adepts.get(i));
            }
        }
    }
}

Example 24 with Placement

use of org.cpsolver.coursett.model.Placement in project cpsolver by UniTime.

the class MinimizeNumberOfUsedRoomsConstraint method estimateLimit.

public int estimateLimit() {
    HashSet<RoomLocation> mandatoryRooms = new HashSet<RoomLocation>();
    for (Lecture lecture : variables()) {
        if (lecture.getNrRooms() == 0)
            continue;
        if (lecture.isCommitted() || lecture.roomLocations().size() == 1)
            mandatoryRooms.addAll(lecture.roomLocations());
    }
    double[][] histogram = new double[iLastDaySlot - iFirstDaySlot + 1][iLastWorkDay - iFirstWorkDay + 1];
    for (int i = 0; i < iLastDaySlot - iFirstDaySlot + 1; i++) for (int j = 0; j < iLastWorkDay - iFirstWorkDay + 1; j++) histogram[i][j] = 0.0;
    for (Lecture lecture : variables()) {
        if (lecture.getNrRooms() == 0)
            continue;
        List<Placement> values = lecture.values(null);
        for (Placement p : lecture.values(null)) {
            int firstSlot = p.getTimeLocation().getStartSlot();
            if (firstSlot > iLastDaySlot)
                continue;
            int endSlot = firstSlot + p.getTimeLocation().getNrSlotsPerMeeting() - 1;
            if (endSlot < iFirstDaySlot)
                continue;
            for (int i = Math.max(firstSlot, iFirstDaySlot); i <= Math.min(endSlot, iLastDaySlot); i++) {
                int dayCode = p.getTimeLocation().getDayCode();
                for (int j = iFirstWorkDay; j <= iLastWorkDay; j++) {
                    if ((dayCode & Constants.DAY_CODES[j]) != 0) {
                        histogram[i - iFirstDaySlot][j - iFirstWorkDay] += ((double) lecture.getNrRooms()) / values.size();
                    }
                }
            }
        }
    }
    int maxAverageRooms = 0;
    for (int i = 0; i < iLastDaySlot - iFirstDaySlot + 1; i++) for (int j = 0; j < iLastWorkDay - iFirstWorkDay + 1; j++) maxAverageRooms = Math.max(maxAverageRooms, (int) Math.ceil(histogram[i][j]));
    return Math.max(1, Math.max(mandatoryRooms.size(), maxAverageRooms));
}

Example 25 with Placement

use of org.cpsolver.coursett.model.Placement in project cpsolver by UniTime.

the class RoomConstraint method inConflict.

@Override
public boolean inConflict(Assignment<Lecture, Placement> assignment, Placement placement) {
    if (!getConstraint())
        return false;
    if (!placement.hasRoomLocation(getResourceId()))
        return false;
    Lecture lecture = placement.variable();
    Placement current = assignment.getValue(lecture);
    int size = lecture.maxRoomUse();
    HashSet<Placement> skipPlacements = null;
    BitSet weekCode = placement.getTimeLocation().getWeekCode();
    RoomConstraintContext context = getContext(assignment);
    for (Enumeration<Integer> e = placement.getTimeLocation().getSlots(); e.hasMoreElements(); ) {
        int slot = e.nextElement();
        for (Placement confPlacement : context.getPlacements(slot)) {
            if (!confPlacement.getTimeLocation().shareWeeks(weekCode))
                continue;
            if (confPlacement.equals(current))
                continue;
            Lecture confLecture = confPlacement.variable();
            if (skipPlacements != null && skipPlacements.contains(confPlacement))
                continue;
            if (confPlacement.canShareRooms(placement) && confLecture.maxRoomUse() + size <= getCapacity()) {
                size += confLecture.maxRoomUse();
                if (skipPlacements == null)
                    skipPlacements = new HashSet<Placement>();
                skipPlacements.add(confPlacement);
                continue;
            }
            return true;
        }
    }
    return false;
}