001    package net.sf.cpsolver.ifs.heuristics;
002    
003    import net.sf.cpsolver.ifs.model.Value;
004    import net.sf.cpsolver.ifs.model.Variable;
005    import net.sf.cpsolver.ifs.solution.Solution;
006    import net.sf.cpsolver.ifs.solver.Solver;
007    
008    /**
009     * Variable selection criterion. <br>
010     * <br>
011     * The IFS algorithm requires a function that selects a variable to be
012     * (re)assigned during the current iteration step. This problem is equivalent to
013     * a variable selection criterion in constraint programming. There are several
014     * guidelines for selecting a variable. In local search, the variable
015     * participating in the largest number of violations is usually selected first.
016     * In backtracking-based algorithms, the first-fail principle is often used,
017     * i.e., a variable whose instantiation is most complicated is selected first.
018     * This could be the variable involved in the largest set of constraints or the
019     * variable with the smallest domain, etc. <br>
020     * <br>
021     * We can split the variable selection criterion into two cases. If some
022     * variables remain unassigned, the �worst� variable among them is selected,
023     * i.e., first-fail principle is applied. This may, for example, be the variable
024     * with the smallest domain or with the highest number of hard and/or soft
025     * constraints. <br>
026     * <br>
027     * The second case occurs when all variables are assigned. Because the algorithm
028     * does not need to stop when a complete feasible solution is found, the
029     * variable selection criterion for such case has to be considered as well. Here
030     * all variables are assigned but the solution is not good enough, e.g., in the
031     * sense of violated soft constraints. We choose a variable whose change of a
032     * value can introduce the best improvement of the solution. It may, for
033     * example, be a variable whose value violates the highest number of soft
034     * constraints. <br>
035     * <br>
036     * It is possible for the solution to become incomplete again after such an
037     * iteration because a value which is not consistent with all hard constraints
038     * can be selected in the value selection criterion. This can also be taken into
039     * account in the variable selection heuristics.
040     * 
041     * @see Solver
042     * 
043     * @version IFS 1.2 (Iterative Forward Search)<br>
044     *          Copyright (C) 2006 - 2010 Tomas Muller<br>
045     *          <a href="mailto:muller@unitime.org">muller@unitime.org</a><br>
046     *          <a href="http://muller.unitime.org">http://muller.unitime.org</a><br>
047     * <br>
048     *          This library is free software; you can redistribute it and/or modify
049     *          it under the terms of the GNU Lesser General Public License as
050     *          published by the Free Software Foundation; either version 3 of the
051     *          License, or (at your option) any later version. <br>
052     * <br>
053     *          This library is distributed in the hope that it will be useful, but
054     *          WITHOUT ANY WARRANTY; without even the implied warranty of
055     *          MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
056     *          Lesser General Public License for more details. <br>
057     * <br>
058     *          You should have received a copy of the GNU Lesser General Public
059     *          License along with this library; if not see <http://www.gnu.org/licenses/>.
060     **/
061    
062    public interface VariableSelection<V extends Variable<V, T>, T extends Value<V, T>> {
063        /** Initialization */
064        public void init(Solver<V, T> solver);
065    
066        /**
067         * Variable selection
068         * 
069         * @param solution
070         *            current solution
071         */
072        public V selectVariable(Solution<V, T> solution);
073    }