    001package org.cpsolver.coursett.heuristics;
    002
    003import java.util.ArrayList;
    004import java.util.Collection;
    005import java.util.List;
    006import java.util.Set;
    007
    008import org.cpsolver.coursett.criteria.TimetablingCriterion;
    009import org.cpsolver.coursett.model.Lecture;
    010import org.cpsolver.coursett.model.Placement;
    011import org.cpsolver.coursett.model.TimetableModel;
    012import org.cpsolver.ifs.assignment.Assignment;
    013import org.cpsolver.ifs.criteria.Criterion;
    014import org.cpsolver.ifs.extension.Extension;
    015import org.cpsolver.ifs.extension.MacPropagation;
    016import org.cpsolver.ifs.heuristics.ValueSelection;
    017import org.cpsolver.ifs.solution.Solution;
    018import org.cpsolver.ifs.solver.Solver;
    019import org.cpsolver.ifs.util.DataProperties;
    020import org.cpsolver.ifs.util.ToolBox;
    021
    022
    023/**
    024 * Placement (value) selection. <br>
    025 * <br>
    026 * We have implemented a hierarchical handling of the value selection criteria
    027 * (see {@link HeuristicSelector}). <br>
    028 * <br>
    029 * The value selection heuristics also allow for random selection of a value
    030 * with a given probability (random walk, e.g., 2%) and, in the case of MPP, to
    031 * select the initial value (if it exists) with a given probability (e.g., 70%). <br>
    032 * <br>
    033 * Parameters (general):
    034 * <table border='1'><caption>Related Solver Parameters</caption>
    035 * <tr>
    036 * <th>Parameter</th>
    037 * <th>Type</th>
    038 * <th>Comment</th>
    039 * </tr>
    040 * <tr>
    041 * <td>Placement.RandomWalkProb</td>
    042 * <td>{@link Double}</td>
    043 * <td>Random walk probability</td>
    044 * </tr>
    045 * <tr>
    046 * <td>Placement.GoodSelectionProb</td>
    047 * <td>{@link Double}</td>
    048 * <td>Good value (not removed from domain) selection probability (MAC related)</td>
    049 * </tr>
    050 * <tr>
    051 * <td>Placement.TabuLength</td>
    052 * <td>{@link Integer}</td>
    053 * <td>Tabu-list length (-1 means do not use tabu-list)</td>
    054 * </tr>
    055 * <tr>
    056 * <td>Placement.MPP_InitialProb</td>
    057 * <td>{@link Double}</td>
    058 * <td>MPP initial selection probability</td>
    059 * </tr>
    060 * <tr>
    061 * <td>Placement.MPP_Limit</td>
    062 * <td>{@link Integer}</td>
    063 * <td>MPP: limit on the number of perturbations (-1 for no limit)</td>
    064 * </tr>
    065 * <tr>
    066 * <td>Placement.MPP_PenaltyLimit</td>
    067 * <td>{@link Double}</td>
    068 * <td>MPP: limit on the perturbations penalty (-1 for no limit)</td>
    069 * </tr>
    070 * </table>
    071 * <br>
    072 * Parameters (for each level of selection):
    073 * <table border='1'><caption>Related Solver Parameters</caption>
    074 * <tr>
    075 * <th>Parameter</th>
    076 * <th>Type</th>
    077 * <th>Comment</th>
    078 * </tr>
    079 * <tr>
    080 * <td>Placement.NrAssignmentsWeight1<br>
    081 * Placement.NrAssignmentsWeight2<br>
    082 * Placement.NrAssignmentsWeight3</td>
    083 * <td>{@link Double}</td>
    084 * <td>Number of previous assignments of the value weight</td>
    085 * </tr>
    086 * <tr>
    087 * <td>Placement.NrConflictsWeight1,2,3</td>
    088 * <td>{@link Double}</td>
    089 * <td>Number of conflicts weight</td>
    090 * </tr>
    091 * <tr>
    092 * <td>Placement.WeightedConflictsWeight1,2,3</td>
    093 * <td>{@link Double}</td>
    094 * <td>Weighted conflicts weight (Conflict-based Statistics related)</td>
    095 * </tr>
    096 * <tr>
    097 * <td>Placement.NrPotentialConflictsWeight1,2,3</td>
    098 * <td>{@link Double}</td>
    099 * <td>Number of potential conflicts weight (Conflict-based Statistics related)</td>
    100 * </tr>
    101 * <tr>
    102 * <td>Placement.MPP_DeltaInitialAssignmentWeight1,2,3</td>
    103 * <td>{@link Double}</td>
    104 * <td>Delta initial assigments weight (MPP, violated initials related)</td>
    105 * </tr>
    106 * <tr>
    107 * <td>Placement.NrHardStudConfsWeight1,2,3</td>
    108 * <td>{@link Double}</td>
    109 * <td>Hard student conflicts weight (student conflicts between single-section
    110 * classes)</td>
    111 * </tr>
    112 * <tr>
    113 * <td>Placement.NrStudConfsWeight1,2,3</td>
    114 * <td>{@link Double}</td>
    115 * <td>Student conflicts weight</td>
    116 * </tr>
    117 * <tr>
    118 * <td>Placement.TimePreferenceWeight1,2,3</td>
    119 * <td>{@link Double}</td>
    120 * <td>Time preference weight</td>
    121 * </tr>
    122 * <tr>
    123 * <td>Placement.DeltaTimePreferenceWeight1,2,3</td>
    124 * <td>{@link Double}</td>
    125 * <td>Time preference delta weight (difference between before and after
    126 * assignemnt of the value)</td>
    127 * </tr>
    128 * <tr>
    129 * <td>Placement.ConstrPreferenceWeight1,2,3</td>
    130 * <td>{@link Double}</td>
    131 * <td>Constraint preference weight</td>
    132 * </tr>
    133 * <tr>
    134 * <td>Placement.RoomPreferenceWeight1,2,3</td>
    135 * <td>{@link Double}</td>
    136 * <td>Room preference weight</td>
    137 * </tr>
    138 * <tr>
    139 * <td>Placement.UselessSlotsWeight1,2,3</td>
    140 * <td>{@link Double}</td>
    141 * <td>Useless slot weight</td>
    142 * </tr>
    143 * <tr>
    144 * <td>Placement.TooBigRoomWeight1,2,3</td>
    145 * <td>{@link Double}</td>
    146 * <td>Too big room weight</td>
    147 * </tr>
    148 * <tr>
    149 * <td>Placement.DistanceInstructorPreferenceWeight1,2,3</td>
    150 * <td>{@link Double}</td>
    151 * <td>Distance (of the rooms of the back-to-back classes) based instructor
    152 * preferences weight</td>
    153 * </tr>
    154 * <tr>
    155 * <td>Placement.DeptSpreadPenaltyWeight1,2,3</td>
    156 * <td>{@link Double}</td>
    157 * <td>Department spreading: penalty of when a slot over initial allowance is
    158 * used</td>
    159 * </tr>
    160 * <tr>
    161 * <td>Placement.ThresholdKoef1,2</td>
    162 * <td>{@link Double}</td>
    163 * <td>Threshold koeficient of the level</td>
    164 * </tr>
    165 * </table>
    166 *
    167 * @see PlacementSelection
    168 * @version CourseTT 1.3 (University Course Timetabling)<br>
    169 *          Copyright (C) 2006 - 2014 Tomáš Müller<br>
    170 *          <a href="mailto:muller@unitime.org">muller@unitime.org</a><br>
    171 *          <a href="http://muller.unitime.org">http://muller.unitime.org</a><br>
    172 * <br>
    173 *          This library is free software; you can redistribute it and/or modify
    174 *          it under the terms of the GNU Lesser General Public License as
    175 *          published by the Free Software Foundation; either version 3 of the
    176 *          License, or (at your option) any later version. <br>
    177 * <br>
    178 *          This library is distributed in the hope that it will be useful, but
    179 *          WITHOUT ANY WARRANTY; without even the implied warranty of
    180 *          MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
    181 *          Lesser General Public License for more details. <br>
    182 * <br>
    183 *          You should have received a copy of the GNU Lesser General Public
    184 *          License along with this library; if not see
    185 *          <a href='http://www.gnu.org/licenses/'>http://www.gnu.org/licenses/</a>.
    186 */
    187
    188public class PlacementSelection implements ValueSelection<Lecture, Placement> {
    189    static final int NR_LEVELS = 3;
    190    private static final double PRECISION = 1.0;
    191    private static boolean USE_THRESHOLD = true;
    192    private boolean iUseThreshold = USE_THRESHOLD;
    193
    194    private double iGoodSelectionProb;
    195    public static final String GOOD_SELECTION_PROB = "Placement.GoodSelectionProb";
    196    private double iRandomWalkProb;
    197    public static final String RW_SELECTION_PROB = "Placement.RandomWalkProb";
    198    private double iInitialSelectionProb;
    199    public static final String INITIAL_SELECTION_PROB = "Placement.MPP_InitialProb";
    200    private int iMPPLimit;
    201    public static final String NR_MPP_LIMIT = "Placement.MPP_Limit";
    202    private double iMPPPenaltyLimit;
    203    public static final String NR_MPP_PENALTY_LIMIT = "Placement.MPP_PenaltyLimit";
    204
    205    private double[] iThresholdKoef = new double[NR_LEVELS];
    206    public static final String NR_THRESHOLD_KOEF = "Placement.ThresholdKoef";
    207
    208    private int iTabuSize = 0;
    209    private ArrayList<Placement> iTabu = null;
    210    private int iTabuPos = 0;
    211    public static final String TABU_LENGTH = "Placement.TabuLength";
    212
    213    private MacPropagation<Lecture, Placement> iProp = null;
    214
    215    private boolean iRW = false;
    216    private boolean iMPP = false;
    217
    218    private boolean iCanUnassingSingleton = false;
    219
    220    @Override
    221    public void init(Solver<Lecture, Placement> solver) {
    222        for (Extension<Lecture, Placement> extension : solver.getExtensions()) {
    223            if (MacPropagation.class.isInstance(extension))
    224                iProp = (MacPropagation<Lecture, Placement>) extension;
    225        }
    226    }
    227
    228    public PlacementSelection(DataProperties properties) {
    229        iMPP = properties.getPropertyBoolean("General.MPP", false);
    230        iRW = properties.getPropertyBoolean("General.RandomWalk", true);
    231        iCanUnassingSingleton = properties.getPropertyBoolean("Placement.CanUnassingSingleton", iCanUnassingSingleton);
    232        iRandomWalkProb = (iRW ? properties.getPropertyDouble(RW_SELECTION_PROB, 0.00) : 0.0);
    233        iGoodSelectionProb = properties.getPropertyDouble(GOOD_SELECTION_PROB, 1.00);
    234        iInitialSelectionProb = (iMPP ? properties.getPropertyDouble(INITIAL_SELECTION_PROB, 0.75) : 0.0);
    235        iMPPLimit = (iMPP ? properties.getPropertyInt(NR_MPP_LIMIT, -1) : -1);
    236        iMPPPenaltyLimit = (iMPP ? properties.getPropertyDouble(NR_MPP_PENALTY_LIMIT, -1.0) : -1.0);
    237        iTabuSize = properties.getPropertyInt(TABU_LENGTH, -1);
    238        if (iTabuSize > 0)
    239            iTabu = new ArrayList<Placement>(iTabuSize);
    240        iUseThreshold = properties.getPropertyBoolean("Placement.UseThreshold", USE_THRESHOLD);
    241        for (int level = 0; level < NR_LEVELS; level++)
    242            iThresholdKoef[level] = (USE_THRESHOLD ? properties.getPropertyDouble(NR_THRESHOLD_KOEF + (level + 1), (level == 0 ? 0.1 : 0.0)) : 0.0);
    243    }
    244
    245    @Override
    246    public Placement selectValue(Solution<Lecture, Placement> solution, Lecture var) {
    247        if (var == null)
    248            return null;
    249        Lecture selectedVariable = var;
    250
    251        TimetableModel model = (TimetableModel) solution.getModel();
    252        Assignment<Lecture, Placement> assignment = solution.getAssignment();
    253        if (selectedVariable.getInitialAssignment() != null) {
    254            if (iMPPLimit >= 0 && model.perturbVariables(assignment).size() >= iMPPLimit) {
    255                if (!containsItselfSingletonOrCommited(model, model.conflictValues(assignment, selectedVariable.getInitialAssignment()), selectedVariable.getInitialAssignment()))
    256                    return selectedVariable.getInitialAssignment();
    257            } else if (iMPPPenaltyLimit >= 0.0 && solution.getPerturbationsCounter() != null && solution.getPerturbationsCounter().getPerturbationPenalty(assignment, model) > iMPPPenaltyLimit) {
    258                if (!containsItselfSingletonOrCommited(model, model.conflictValues(assignment, selectedVariable.getInitialAssignment()), selectedVariable.getInitialAssignment()))
    259                    return selectedVariable.getInitialAssignment();
    260            } else if (selectedVariable.getInitialAssignment() != null && ToolBox.random() <= iInitialSelectionProb) {
    261                if (!containsItselfSingletonOrCommited(model, model.conflictValues(assignment, selectedVariable.getInitialAssignment()), selectedVariable.getInitialAssignment()))
    262                    return selectedVariable.getInitialAssignment();
    263            }
    264        }
    265
    266        List<Placement> values = selectedVariable.values(solution.getAssignment());
    267        if (iRW && ToolBox.random() <= iRandomWalkProb) {
    268            for (int i = 0; i < 5; i++) {
    269                Placement ret = ToolBox.random(values);
    270                if (!containsItselfSingletonOrCommited(model, model.conflictValues(assignment, ret), ret))
    271                    return ret;
    272            }
    273        }
    274        Placement current = assignment.getValue(selectedVariable);
    275        if (iProp != null && current == null && ToolBox.random() <= iGoodSelectionProb) {
    276            Collection<Placement> goodValues = iProp.goodValues(assignment, selectedVariable);
    277            if (!goodValues.isEmpty())
    278                values = new ArrayList<Placement>(goodValues);
    279        }
    280        if (values.size() == 1) {
    281            Placement ret = values.get(0);
    282            if (!containsItselfSingletonOrCommited(model, model.conflictValues(assignment, ret), ret))
    283                return ret;
    284        }
    285
    286        long[] bestCost = new long[NR_LEVELS];
    287        List<Placement> selectionValues = null;
    288
    289        HeuristicSelector<Placement> selector = (iUseThreshold ? new HeuristicSelector<Placement>(iThresholdKoef) : null);
    290        for (Placement value : values) {
    291            if (iTabu != null && iTabu.contains(value))
    292                continue;
    293            if (current != null && current.equals(value))
    294                continue;
    295
    296            Set<Placement> conflicts = value.variable().getModel().conflictValues(assignment, value);
    297
    298            if (containsItselfSingletonOrCommited(model, conflicts, value))
    299                continue;
    300
    301            if (iUseThreshold) {
    302                Double flt = selector.firstLevelThreshold();
    303                double[] costs = new double[NR_LEVELS];
    304                for (int level = 0; level < NR_LEVELS; level++) {
    305                    costs[level] = getCost(assignment, level, value, conflicts);
    306                    if (level == 0 && flt != null && costs[0] > flt.doubleValue()) {
    307                        break;
    308                    }
    309                }
    310                if (flt != null && costs[0] > flt.doubleValue())
    311                    continue;
    312                selector.add(costs, value);
    313            } else {
    314                boolean fail = false;
    315                boolean best = false;
    316                for (int level = 0; !fail && level < 1; level++) {
    317                    double val = getCost(assignment, level, value, conflicts);
    318                    long cost = Math.round(PRECISION * val);
    319                    if (selectionValues != null && !best) {
    320                        if (cost > bestCost[level]) {
    321                            fail = true;
    322                        }
    323                        if (cost < bestCost[level]) {
    324                            bestCost[level] = cost;
    325                            selectionValues.clear();
    326                            best = true;
    327                        }
    328                    } else {
    329                        bestCost[level] = cost;
    330                    }
    331                }
    332                if (selectionValues == null)
    333                    selectionValues = new ArrayList<Placement>(values.size());
    334                if (!fail)
    335                    selectionValues.add(value);
    336            }
    337        }
    338        // ToolBox.print("Best "+selectionValues.size()+" locations for variable "+selectedVariable.getId()+" have "+bestConflicts+" conflicts ("+bestRemovals+" weighted) and "+bestStudentConflicts+" ("+bestOriginalStudentConflicts+" * "+bestKoef+" + "+bestPenalty+") preference.");
    339        Placement selectedValue = null;
    340        if (iUseThreshold) {
    341            List<HeuristicSelector<Placement>.Element> selectionElements = selector.selection();
    342
    343            if (selectedVariable.getInitialAssignment() != null) {
    344                for (HeuristicSelector<Placement>.Element element : selectionElements) {
    345                    Placement value = element.getObject();
    346                    if (value.equals(selectedVariable.getInitialAssignment())) {
    347                        selectedValue = value;
    348                        break;
    349                    }
    350                }
    351                // &&
    352                // selectionValues.contains(selectedVariable.getInitialAssignment()))
    353                // return selectedVariable.getInitialAssignment();
    354            }
    355
    356            if (selectedValue == null) {
    357                HeuristicSelector<Placement>.Element selection = ToolBox.random(selectionElements);
    358                selectedValue = (selection == null ? null : selection.getObject());
    359            }
    360        } else {
    361            if (selectedVariable.getInitialAssignment() != null
    362                    && selectionValues.contains(selectedVariable.getInitialAssignment()))
    363                return selectedVariable.getInitialAssignment();
    364            selectedValue = ToolBox.random(selectionValues);
    365        }
    366        if (selectedValue != null && iTabu != null) {
    367            if (iTabu.size() == iTabuPos)
    368                iTabu.add(selectedValue);
    369            else
    370                iTabu.set(iTabuPos, selectedValue);
    371            iTabuPos = (iTabuPos + 1) % iTabuSize;
    372        }
    373        return selectedValue;
    374    }
    375
    376    public boolean containsItselfSingletonOrCommited(TimetableModel model, Set<Placement> values,
    377            Placement selectedValue) {
    378        if (values.contains(selectedValue))
    379            return true;
    380        if (model.hasConstantVariables()) {
    381            for (Placement placement : values) {
    382                Lecture lecture = placement.variable();
    383                if (lecture.isCommitted())
    384                    return true;
    385                if (!iCanUnassingSingleton && lecture.isSingleton())
    386                    return true;
    387            }
    388            return false;
    389        } else {
    390            if (iCanUnassingSingleton)
    391                return false;
    392            for (Placement placement : values) {
    393                Lecture lecture = placement.variable();
    394                if (lecture.isSingleton())
    395                    return true;
    396            }
    397            return false;
    398        }
    399    }
    400
    401    private double getCost(Assignment<Lecture, Placement> assignment, int level, Placement value, Set<Placement> conflicts) {
    402        double ret = 0.0;
    403        for (Criterion<Lecture, Placement> criterion: value.variable().getModel().getCriteria()) {
    404            if (criterion instanceof TimetablingCriterion) {
    405                double w = ((TimetablingCriterion)criterion).getPlacementSelectionWeight(level, assignment.getIndex() - 1);
    406                if (w != 0.0)
    407                    ret += w * criterion.getValue(assignment, value, conflicts);
    408            } else {
    409                ret += criterion.getWeightedValue(assignment, value, conflicts);
    410            }
    411        }
    412        return ret;
    413    }
    414
    415}