/**
*
* Solves the Minesweeper problems.
*
* See http://www.hakank.org/google_or_tools/minesweeper.py
*
*/
private static void Solve()
{
Solver solver = new Solver("Minesweeper");
//
// data
//
int[] S = { -1, 0, 1 };
Console.WriteLine("Problem:");
for (int i = 0; i < r; i++)
{
for (int j = 0; j < c; j++)
{
if (game[i, j] > X)
{
Console.Write(game[i, j] + " ");
}
else
{
Console.Write("X ");
}
}
Console.WriteLine();
}
Console.WriteLine();
//
// Decision variables
//
IntVar[,] mines = solver.MakeIntVarMatrix(r, c, 0, 1, "mines");
// for branching
IntVar[] mines_flat = mines.Flatten();
//
// Constraints
//
for (int i = 0; i < r; i++)
{
for (int j = 0; j < c; j++)
{
if (game[i, j] >= 0)
{
solver.Add(mines[i, j] == 0);
// this cell is the sum of all its neighbours
var tmp = from a in S from b in S
where i +
a >= 0 && j + b >= 0 &&
i + a < r && j + b < c select(mines[i + a, j + b]);
solver.Add(tmp.ToArray().Sum() == game[i, j]);
}
if (game[i, j] > X)
{
// This cell cannot be a mine since it
// has some value assigned to it
solver.Add(mines[i, j] == 0);
}
}
}
//
// Search
//
DecisionBuilder db = solver.MakePhase(mines_flat, Solver.CHOOSE_PATH, Solver.ASSIGN_MIN_VALUE);
solver.NewSearch(db);
int sol = 0;
while (solver.NextSolution())
{
sol++;
Console.WriteLine("Solution #{0} ", sol + " ");
for (int i = 0; i < r; i++)
{
for (int j = 0; j < c; j++)
{
Console.Write("{0} ", mines[i, j].Value());
}
Console.WriteLine();
}
Console.WriteLine();
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
public static void Main(String[] args)
{
// Instantiate the solver.
// [START solver]
Solver solver = new Solver("CP is fun!");
// [END solver]
// [START variables]
const int kBase = 10;
// Decision variables.
IntVar c = solver.MakeIntVar(1, kBase - 1, "C");
IntVar p = solver.MakeIntVar(0, kBase - 1, "P");
IntVar i = solver.MakeIntVar(1, kBase - 1, "I");
IntVar s = solver.MakeIntVar(0, kBase - 1, "S");
IntVar f = solver.MakeIntVar(1, kBase - 1, "F");
IntVar u = solver.MakeIntVar(0, kBase - 1, "U");
IntVar n = solver.MakeIntVar(0, kBase - 1, "N");
IntVar t = solver.MakeIntVar(1, kBase - 1, "T");
IntVar r = solver.MakeIntVar(0, kBase - 1, "R");
IntVar e = solver.MakeIntVar(0, kBase - 1, "E");
// Group variables in a vector so that we can use AllDifferent.
IntVar[] letters = new IntVar[] { c, p, i, s, f, u, n, t, r, e };
// Verify that we have enough digits.
if (kBase < letters.Length)
{
throw new Exception("kBase < letters.Length");
}
// [END variables]
// Define constraints.
// [START constraints]
solver.Add(letters.AllDifferent());
// CP + IS + FUN = TRUE
solver.Add(p + s + n + kBase * (c + i + u) + kBase * kBase * f ==
e + kBase * u + kBase * kBase * r + kBase * kBase * kBase * t);
// [END constraints]
// [START solve]
int SolutionCount = 0;
// Create the decision builder to search for solutions.
DecisionBuilder db = solver.MakePhase(letters, Solver.CHOOSE_FIRST_UNBOUND, Solver.ASSIGN_MIN_VALUE);
solver.NewSearch(db);
while (solver.NextSolution())
{
Console.Write("C=" + c.Value() + " P=" + p.Value());
Console.Write(" I=" + i.Value() + " S=" + s.Value());
Console.Write(" F=" + f.Value() + " U=" + u.Value());
Console.Write(" N=" + n.Value() + " T=" + t.Value());
Console.Write(" R=" + r.Value() + " E=" + e.Value());
Console.WriteLine();
// Is CP + IS + FUN = TRUE?
if (p.Value() + s.Value() + n.Value() + kBase * (c.Value() + i.Value() + u.Value()) +
kBase * kBase * f.Value() !=
e.Value() + kBase * u.Value() + kBase * kBase * r.Value() + kBase * kBase * kBase * t.Value())
{
throw new Exception("CP + IS + FUN != TRUE");
}
SolutionCount++;
}
solver.EndSearch();
Console.WriteLine($"Number of solutions found: {SolutionCount}");
// [END solve]
}
/**
*
* Rogo puzzle solver.
*
* From http://www.rogopuzzle.co.nz/
* """
* The object is to collect the biggest score possible using a given
* number of steps in a loop around a grid. The best possible score
* for a puzzle is given with it, so you can easily check that you have
* solved the puzzle. Rogo puzzles can also include forbidden squares,
* which must be avoided in your loop.
* """
*
* Also see Mike Trick:
* "Operations Research, Sudoko, Rogo, and Puzzles"
* http://mat.tepper.cmu.edu/blog/?p=1302
*
*
* Also see, http://www.hakank.org/or-tools/rogo2.py
* though this model differs in a couple of central points
* which makes it much faster:
*
* - it use a table (
* AllowedAssignments) with the valid connections
* - instead of two coordinates arrays, it use a single path array
*
*/
private static void Solve()
{
Solver solver = new Solver("Rogo2");
Console.WriteLine("\n");
Console.WriteLine("**********************************************");
Console.WriteLine(" {0}", problem_name);
Console.WriteLine("**********************************************\n");
//
// Data
//
int B = -1;
Console.WriteLine("Rows: {0} Cols: {1} Max Steps: {2}", rows, cols, max_steps);
int[] problem_flatten = problem.Cast <int>().ToArray();
int max_point = problem_flatten.Max();
int max_sum = problem_flatten.Sum();
Console.WriteLine("max_point: {0} max_sum: {1} best: {2}", max_point, max_sum, best);
IEnumerable <int> STEPS = Enumerable.Range(0, max_steps);
IEnumerable <int> STEPS1 = Enumerable.Range(0, max_steps - 1);
// the valid connections, to be used with AllowedAssignments
IntTupleSet valid_connections = ValidConnections(rows, cols);
//
// Decision variables
//
IntVar[] path = solver.MakeIntVarArray(max_steps, 0, rows * cols - 1, "path");
IntVar[] points = solver.MakeIntVarArray(max_steps, 0, best, "points");
IntVar sum_points = points.Sum().VarWithName("sum_points");
//
// Constraints
//
foreach (int s in STEPS)
{
// calculate the points (to maximize)
solver.Add(points[s] == problem_flatten.Element(path[s]));
// ensure that there are no black cells in
// the path
solver.Add(problem_flatten.Element(path[s]) != B);
}
solver.Add(path.AllDifferent());
// valid connections
foreach (int s in STEPS1)
{
solver.Add(new IntVar[] { path[s], path[s + 1] }.AllowedAssignments(valid_connections));
}
// around the corner
solver.Add(new IntVar[] { path[max_steps - 1], path[0] }.AllowedAssignments(valid_connections));
// Symmetry breaking
for (int s = 1; s < max_steps; s++)
{
solver.Add(path[0] < path[s]);
}
//
// Objective
//
OptimizeVar obj = sum_points.Maximize(1);
//
// Search
//
DecisionBuilder db = solver.MakePhase(path, Solver.INT_VAR_DEFAULT, Solver.INT_VALUE_DEFAULT);
solver.NewSearch(db, obj);
while (solver.NextSolution())
{
Console.WriteLine("sum_points: {0}", sum_points.Value());
Console.Write("path: ");
foreach (int s in STEPS)
{
Console.Write("{0} ", path[s].Value());
}
Console.WriteLine();
Console.WriteLine("(Adding 1 to coords...)");
int[,] sol = new int[rows, cols];
foreach (int s in STEPS)
{
int p = (int)path[s].Value();
int x = (int)(p / cols);
int y = (int)(p % cols);
Console.WriteLine("{0,2},{1,2} ({2} points)", x + 1, y + 1, points[s].Value());
sol[x, y] = 1;
}
Console.WriteLine("\nThe path is marked by 'X's:");
for (int i = 0; i < rows; i++)
{
for (int j = 0; j < cols; j++)
{
String p = sol[i, j] == 1 ? "X" : " ";
String q = problem[i, j] == B ? "B" : problem[i, j] == 0 ? "." : problem[i, j].ToString();
Console.Write("{0,2}{1} ", q, p);
}
Console.WriteLine();
}
Console.WriteLine();
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Simple regular expression.
*
* My last name (Kjellerstrand) is quite often misspelled
* in ways that this regular expression shows:
* k(je|ä)ll(er|ar)?(st|b)r?an?d
*
* This model generates all the words that can be construed
* by this regular expression.
*
*
* Also see http://www.hakank.org/or-tools/regex.py
*
*/
private static void Solve(int n, List <String> res)
{
Solver solver = new Solver("RegexGeneration");
Console.WriteLine("\nn: {0}", n);
// The DFS (for regular)
int n_states = 11;
int input_max = 12;
int initial_state = 1; // 0 is for the failing state
int[] accepting_states = { 12 };
// The DFA
int[,] transition_fn =
{
// 1 2 3 4 5 6 7 8 9 0 1 2 //
{ 0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, // 1 k
{ 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0 }, // 2 je
{ 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0 }, // 3 ä
{ 0, 0, 0, 0, 5, 6, 7, 8, 0, 0, 0, 0 }, // 4 ll
{ 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0 }, // 5 er
{ 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0 }, // 6 ar
{ 0, 0, 0, 0, 0, 0, 0, 0, 9, 10, 0, 0 }, // 7 st
{ 0, 0, 0, 0, 0, 0, 0, 0, 9, 10, 0, 0 }, // 8 b
{ 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0 }, // 9 r
{ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 12 }, // 10 a
{ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12 }, // 11 n
// 12 d
};
// Name of the states
String[] s = { "k", "je", "ä", "ll", "er", "ar", "st", "b", "r", "a", "n", "d" };
//
// Decision variables
//
IntVar[] x = solver.MakeIntVarArray(n, 1, input_max, "x");
//
// Constraints
//
MyRegular(solver, x, n_states, input_max, transition_fn, initial_state, accepting_states);
//
// Search
//
DecisionBuilder db = solver.MakePhase(x, Solver.CHOOSE_FIRST_UNBOUND, Solver.ASSIGN_MIN_VALUE);
solver.NewSearch(db);
while (solver.NextSolution())
{
List <String> res2 = new List <String>();
// State 1 (the start state) is not included in the
// state array (x) so we add it first.
res2.Add(s[0]);
for (int i = 0; i < n; i++)
{
res2.Add(s[x[i].Value() - 1]);
}
res.Add(String.Join("", res2.ToArray()));
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Solves the N-Queens problem.
*
* Syntax: nqueens.exe n num print
* where
* n : size of board
* num : number of solutions to calculate
* print: print the results (if > 0)
*
*/
private static void Solve(int n = 8, int num = 0, int print = 1)
{
Solver solver = new Solver("N-Queens");
//
// Decision variables
//
IntVar[] q = solver.MakeIntVarArray(n, 0, n - 1, "q");
//
// Constraints
//
solver.Add(q.AllDifferent());
IntVar[] q1 = new IntVar[n];
IntVar[] q2 = new IntVar[n];
for (int i = 0; i < n; i++)
{
q1[i] = (q[i] + i).Var();
q2[i] = (q[i] - i).Var();
}
solver.Add(q1.AllDifferent());
solver.Add(q2.AllDifferent());
// Alternative version: it works as well but are not that clear
/*
* solver.Add((from i in Enumerable.Range(0, n)
* select (q[i] + i).Var()).ToArray().AllDifferent());
*
* solver.Add((from i in Enumerable.Range(0, n)
* select (q[i] - i).Var()).ToArray().AllDifferent());
*/
//
// Search
//
DecisionBuilder db =
solver.MakePhase(q, Solver.CHOOSE_MIN_SIZE_LOWEST_MAX, Solver.ASSIGN_CENTER_VALUE);
solver.NewSearch(db);
int c = 0;
while (solver.NextSolution())
{
if (print > 0)
{
for (int i = 0; i < n; i++)
{
Console.Write("{0} ", q[i].Value());
}
Console.WriteLine();
}
c++;
if (num > 0 && c >= num)
{
break;
}
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Sicherman Dice.
*
* From http://en.wikipedia.org/wiki/Sicherman_dice
* ""
* Sicherman dice are the only pair of 6-sided dice which are not normal dice,
* bear only positive integers, and have the same probability distribution for
* the sum as normal dice.
*
* The faces on the dice are numbered 1, 2, 2, 3, 3, 4 and 1, 3, 4, 5, 6, 8.
* ""
*
* I read about this problem in a book/column by Martin Gardner long
* time ago, and got inspired to model it now by the WolframBlog post
* "Sicherman Dice": http://blog.wolfram.com/2010/07/13/sicherman-dice/
*
* This model gets the two different ways, first the standard way and
* then the Sicherman dice:
*
* x1 = [1, 2, 3, 4, 5, 6]
* x2 = [1, 2, 3, 4, 5, 6]
* ----------
* x1 = [1, 2, 2, 3, 3, 4]
* x2 = [1, 3, 4, 5, 6, 8]
*
*
* Extra: If we also allow 0 (zero) as a valid value then the
* following two solutions are also valid:
*
* x1 = [0, 1, 1, 2, 2, 3]
* x2 = [2, 4, 5, 6, 7, 9]
* ----------
* x1 = [0, 1, 2, 3, 4, 5]
* x2 = [2, 3, 4, 5, 6, 7]
*
* These two extra cases are mentioned here:
* http://mathworld.wolfram.com/SichermanDice.html
*
*
* Also see http://www.hakank.org/or-tools/sicherman_dice.py
*
*/
private static void Solve()
{
Solver solver = new Solver("SichermanDice");
//
// Data
//
int n = 6;
int m = 10;
int lowest_value = 0;
// standard distribution
int[] standard_dist = { 1, 2, 3, 4, 5, 6, 5, 4, 3, 2, 1 };
IEnumerable <int> RANGE = Enumerable.Range(0, n);
IEnumerable <int> RANGE1 = Enumerable.Range(0, n - 1);
//
// Decision variables
//
IntVar[] x1 = solver.MakeIntVarArray(n, lowest_value, m, "x1");
IntVar[] x2 = solver.MakeIntVarArray(n, lowest_value, m, "x2");
//
// Constraints
//
for (int k = 0; k < standard_dist.Length; k++)
{
solver.Add((from i in RANGE
from j in RANGE
select x1[i] + x2[j] == k + 2
).ToArray().Sum() == standard_dist[k]);
}
// symmetry breaking
foreach (int i in RANGE1)
{
solver.Add(x1[i] <= x1[i + 1]);
solver.Add(x2[i] <= x2[i + 1]);
solver.Add(x1[i] <= x2[i]);
}
//
// Search
//
DecisionBuilder db = solver.MakePhase(x1.Concat(x2).ToArray(),
Solver.INT_VAR_DEFAULT,
Solver.INT_VALUE_DEFAULT);
solver.NewSearch(db);
while (solver.NextSolution())
{
Console.Write("x1: ");
foreach (int i in RANGE)
{
Console.Write(x1[i].Value() + " ");
}
Console.Write("\nx2: ");
foreach (int i in RANGE)
{
Console.Write(x2[i].Value() + " ");
}
Console.WriteLine("\n");
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Secret Santa problem in Google CP Solver.
*
* From Ruby Quiz Secret Santa
* http://www.rubyquiz.com/quiz2.html
* """
* Honoring a long standing tradition started by my wife's dad, my friends
* all play a Secret Santa game around Christmas time. We draw names and
* spend a week sneaking that person gifts and clues to our identity. On the
* last night of the game, we get together, have dinner, share stories, and,
* most importantly, try to guess who our Secret Santa was. It's a crazily
* fun way to enjoy each other's company during the holidays.
*
* To choose Santas, we use to draw names out of a hat. This system was
* tedious, prone to many 'Wait, I got myself...' problems. This year, we
* made a change to the rules that further complicated picking and we knew
* the hat draw would not stand up to the challenge. Naturally, to solve
* this problem, I scripted the process. Since that turned out to be more
* interesting than I had expected, I decided to share.
*
* This weeks Ruby Quiz is to implement a Secret Santa selection script.
* * Your script will be fed a list of names on STDIN.
* ...
* Your script should then choose a Secret Santa for every name in the list.
* Obviously, a person cannot be their own Secret Santa. In addition, my friends
* no longer allow people in the same family to be Santas for each other and your
* script should take this into account.
* """
*
* Comment: This model skips the file input and mail parts. We
* assume that the friends are identified with a number from 1..n,
* and the families is identified with a number 1..num_families.
*
* Also see http://www.hakank.org/or-tools/secret_santa.py
* Also see http://www.hakank.org/or-tools/secret_santa2.cs
*
*/
private static void Solve()
{
Solver solver = new Solver("SecretSanta");
int[] family = { 1, 1, 1, 1, 2, 3, 3, 3, 3, 3, 4, 4 };
int n = family.Length;
Console.WriteLine("n = {0}", n);
IEnumerable <int> RANGE = Enumerable.Range(0, n);
//
// Decision variables
//
IntVar[] x = solver.MakeIntVarArray(n, 0, n - 1, "x");
//
// Constraints
//
solver.Add(x.AllDifferent());
// Can't be one own"s Secret Santa
// (i.e. ensure that there are no fix-point in the array.)
foreach (int i in RANGE)
{
solver.Add(x[i] != i);
}
// No Secret Santa to a person in the same family
foreach (int i in RANGE)
{
solver.Add(solver.MakeIntConst(family[i]) != family.Element(x[i]));
}
//
// Search
//
DecisionBuilder db = solver.MakePhase(x,
Solver.INT_VAR_SIMPLE,
Solver.INT_VALUE_SIMPLE);
solver.NewSearch(db);
while (solver.NextSolution())
{
Console.Write("x: ");
foreach (int i in RANGE)
{
Console.Write(x[i].Value() + " ");
}
Console.WriteLine();
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Eq 10 in Google CP Solver.
*
* Standard benchmark problem.
*
* Also see http://hakank.org/or-tools/eq10.py
*
*/
private static void Solve()
{
Solver solver = new Solver("Eq10");
int n = 7;
//
// Decision variables
//
IntVar X1 = solver.MakeIntVar(0, 10, "X1");
IntVar X2 = solver.MakeIntVar(0, 10, "X2");
IntVar X3 = solver.MakeIntVar(0, 10, "X3");
IntVar X4 = solver.MakeIntVar(0, 10, "X4");
IntVar X5 = solver.MakeIntVar(0, 10, "X5");
IntVar X6 = solver.MakeIntVar(0, 10, "X6");
IntVar X7 = solver.MakeIntVar(0, 10, "X7");
IntVar[] X = { X1, X2, X3, X4, X5, X6, X7 };
//
// Constraints
//
solver.Add(0 + 98527 * X1 + 34588 * X2 + 5872 * X3 + 59422 * X5 + 65159 * X7
== 1547604 + 30704 * X4 + 29649 * X6);
solver.Add(0 + 98957 * X2 + 83634 * X3 + 69966 * X4 + 62038 * X5 + 37164 * X6 + 85413 * X7
== 1823553 + 93989 * X1);
solver.Add(900032 + 10949 * X1 + 77761 * X2 + 67052 * X5
== 0 + 80197 * X3 + 61944 * X4 + 92964 * X6 + 44550 * X7);
solver.Add(0 + 73947 * X1 + 84391 * X3 + 81310 * X5
== 1164380 + 96253 * X2 + 44247 * X4 + 70582 * X6 + 33054 * X7);
solver.Add(0 + 13057 * X3 + 42253 * X4 + 77527 * X5 + 96552 * X7
== 1185471 + 60152 * X1 + 21103 * X2 + 97932 * X6);
solver.Add(1394152 + 66920 * X1 + 55679 * X4
== 0 + 64234 * X2 + 65337 * X3 + 45581 * X5 + 67707 * X6 + 98038 * X7);
solver.Add(0 + 68550 * X1 + 27886 * X2 + 31716 * X3 + 73597 * X4 + 38835 * X7
== 279091 + 88963 * X5 + 76391 * X6);
solver.Add(0 + 76132 * X2 + 71860 * X3 + 22770 * X4 + 68211 * X5 + 78587 * X6
== 480923 + 48224 * X1 + 82817 * X7);
solver.Add(519878 + 94198 * X2 + 87234 * X3 + 37498 * X4
== 0 + 71583 * X1 + 25728 * X5 + 25495 * X6 + 70023 * X7);
solver.Add(361921 + 78693 * X1 + 38592 * X5 + 38478 * X6
== 0 + 94129 * X2 + 43188 * X3 + 82528 * X4 + 69025 * X7);
//
// Search
//
DecisionBuilder db = solver.MakePhase(X,
Solver.INT_VAR_DEFAULT,
Solver.INT_VALUE_DEFAULT);
solver.NewSearch(db);
while (solver.NextSolution())
{
for (int i = 0; i < n; i++)
{
Console.Write(X[i].ToString() + " ");
}
Console.WriteLine();
}
Console.WriteLine("\nSolutions: " + solver.Solutions());
Console.WriteLine("WallTime: " + solver.WallTime() + "ms ");
Console.WriteLine("Failures: " + solver.Failures());
Console.WriteLine("Branches: " + solver.Branches());
solver.EndSearch();
}
/**
*
* Max flow problem.
*
* From Winston 'Operations Research', page 420f, 423f
* Sunco Oil example.
*
*
* Also see http://www.hakank.org/or-tools/max_flow_winston1.py
*
*/
private static void Solve()
{
Solver solver = new Solver("MaxFlowWinston1");
//
// Data
//
int n = 5;
IEnumerable <int> NODES = Enumerable.Range(0, n);
// The arcs
// Note:
// This is 1-based to be compatible with other implementations.
//
int[,] arcs1 =
{
{ 1, 2 },
{ 1, 3 },
{ 2, 3 },
{ 2, 4 },
{ 3, 5 },
{ 4, 5 },
{ 5, 1 }
};
// Capacities
int [] cap = { 2, 3, 3, 4, 2, 1, 100 };
// Convert arcs to 0-based
int num_arcs = arcs1.GetLength(0);
IEnumerable <int> ARCS = Enumerable.Range(0, num_arcs);
int[,] arcs = new int[num_arcs, 2];
foreach (int i in ARCS)
{
for (int j = 0; j < 2; j++)
{
arcs[i, j] = arcs1[i, j] - 1;
}
}
// Convert arcs to matrix (for sanity checking below)
int[,] mat = new int[num_arcs, num_arcs];
foreach (int i in NODES)
{
foreach (int j in NODES)
{
int c = 0;
foreach (int k in ARCS)
{
if (arcs[k, 0] == i && arcs[k, 1] == j)
{
c = 1;
}
}
mat[i, j] = c;
}
}
//
// Decision variables
//
IntVar[,] flow = solver.MakeIntVarMatrix(n, n, 0, 200, "flow");
IntVar z = flow[n - 1, 0].VarWithName("z");
//
// Constraints
//
// capacity of arcs
foreach (int i in ARCS)
{
solver.Add(flow[arcs[i, 0], arcs[i, 1]] <= cap[i]);
}
// inflows == outflows
foreach (int i in NODES)
{
var s1 = (from k in ARCS
where arcs[k, 1] == i
select flow[arcs[k, 0], arcs[k, 1]]
).ToArray().Sum();
var s2 = (from k in ARCS
where arcs[k, 0] == i
select flow[arcs[k, 0], arcs[k, 1]]
).ToArray().Sum();
solver.Add(s1 == s2);
}
// Sanity check: just arcs with connections can have a flow.
foreach (int i in NODES)
{
foreach (int j in NODES)
{
if (mat[i, j] == 0)
{
solver.Add(flow[i, j] == 0);
}
}
}
//
// Objective
//
OptimizeVar obj = z.Maximize(1);
//
// Search
//
DecisionBuilder db = solver.MakePhase(flow.Flatten(),
Solver.INT_VAR_DEFAULT,
Solver.ASSIGN_MAX_VALUE);
solver.NewSearch(db, obj);
while (solver.NextSolution())
{
Console.WriteLine("z: {0}", z.Value());
foreach (int i in NODES)
{
foreach (int j in NODES)
{
Console.Write(flow[i, j].Value() + " ");
}
Console.WriteLine();
}
Console.WriteLine();
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* This model was inspired by David Curran's
* blog post "The Fairest Way to Pick a Team "
* http://liveatthewitchtrials.blogspot.se/2012/06/fairest-way-to-pick-team.html
* """
* What is the best way to pick a team? As kids we would always strictly alternate
* between teams so team 1 had first team 2 the second pick and then team 1 again etc.
*
* Most things you can measure about people are on a bell curve. A small number of
* people are bad, most are in the middle and a few are good. There are a few good
* known metrics of ability. None are perfect, there is no one number that can sum up
* ability. The simpler the sport the more one metric can tell you, in cycling VO2 max is
* a very good indicator. Whereas in soccer VO2 max, kicking speed, vertical leap, number
* of keep me ups you can do etc could all measure some part of football ability.
*
* So say there was one good metric for a task and teams were picked based on this.
* Is the standard strict alteration, where Team 1 picks then Team 2 alternating, fair?
* Fair here meaning both teams end up with a similar quality.
* """
*
* This version has two changes compared to http://www.hakank.org/or-tools/picking_teams.cs
* - there can be more than 2 teams
* - it don't have to be exactly the same number of members in each team
*
*
* Model by Hakan Kjellerstrand ([email protected])
*
* See other or-tools/C# models at http://www.hakank.org/or-tools/#csharp
*
*/
private static void Solve(int n = 10, int num_teams = 2, int team_diff = 1, int max = 10, int sols_to_show = 0)
{
Solver solver = new Solver("PickingTeams");
Console.WriteLine("n : " + n);
Console.WriteLine("num_teams: " + num_teams); // New
Console.WriteLine("team_diff: " + team_diff); // New
Console.WriteLine("max : " + max);
Console.WriteLine("sols_to_show: " + sols_to_show);
// New: We skip this check
// if (n % num_teams != 0) {
// Console.WriteLine("The number of people (n) must be divisible by 2.");
// System.Environment.Exit(1);
// }
//
// Data
//
// Randomize data:
int seed = (int)DateTime.Now.Ticks;
Random generator = new Random(seed);
int[] s = new int[n];
for (int i = 0; i < n; i++)
{
s[i] = 1 + generator.Next(max);
if (n <= 100)
{
Console.Write(s[i] + " ");
}
}
Console.WriteLine();
Console.WriteLine("\n" + n + " numbers generated\n");
int the_sum = s.Sum();
int half_sum = (int)the_sum / num_teams;
Console.WriteLine("sum: " + the_sum + " half_sum: " + half_sum);
IEnumerable <int> NRange = Enumerable.Range(0, n);
//
// Decision variables
//
// To which team (s) do x[i] belong?
// New: Added num_teams-1
IntVar[] x = solver.MakeIntVarArray(n, 0, num_teams - 1, "x");
// The old version for 2 teams
// IntVar diff = (
// (from k in NRange select (s[k]*(x[k] == 0)) ).ToArray().Sum() -
// (from k in NRange select (s[k]*(x[k] == 1)) ).ToArray().Sum()
// ).Abs().Var();
// New:
// Calculate the team sums
// Note: It is more efficient to restrict lower and upper bounds of the team sums.
// Here I arbitrarily allow a slack of
// sum +/- (half_sum/num_teams)
// For specific applications this might be adjusted.
int n3 = half_sum / num_teams;
Console.WriteLine("n3: " + n3);
Console.WriteLine("half_sum-n3 " + (half_sum - n3) + "... half_sum+n3: " + (half_sum + n3));
IntVar[] team_sum = solver.MakeIntVarArray(num_teams, half_sum - n3, half_sum + n3, "team_sum");
for (int team = 0; team < num_teams; team++)
{
team_sum[team] = (from k in NRange select(s[k] * (x[k] == team))).ToArray().Sum().Var();
}
// New:
// Calculate the total number of difference points between the
// teams (to be minimized)
IntVar diff = (from t1 in Enumerable.Range(0, num_teams)
from t2 in Enumerable.Range(0, num_teams)
where t1 < t2
select(team_sum[t1] - team_sum[t2]).Abs().Var()).ToArray().Sum().Var();
Console.WriteLine("diff.Max(): " + diff.Max());
//
// New: Number of members in each team
//
// Note that we restrict the possible values to +/- team_diff.
int n2 = (int)n / num_teams;
Console.WriteLine("n2: " + n2);
IntVar[] team_num = solver.MakeIntVarArray(num_teams, n2 - team_diff, n2 + team_diff, "team_num");
for (int team = 0; team < num_teams; team++)
{
team_num[team] = (from k in NRange select(x[k] == team)).ToArray().Sum().Var();
}
// New: We send all the IntVar arrays to the solver
IntVar[] all = x.Concat(team_sum).Concat(team_num).ToArray();
//
// Constraints
//
// New: Ensure that there are the (about) same number of people in each team.
for (int t = 0; t < num_teams; t++)
{
solver.Add((((from k in NRange select(x[k] == t)).ToArray().Sum() - n2)).Abs() <= team_diff);
}
// The teams_sums should add up to the total sum
solver.Add(team_sum.Sum() == the_sum);
// symmetry breaking: assign first member to team 0
solver.Add(x[0] == 0);
// Odd sum must yield odd diff, even sum yield even diff
IntVar even_odd = solver.MakeIntConst(the_sum % 2);
solver.Add(solver.MakeModuloConstraint(diff, 2, even_odd));
//
// Search
//
DecisionBuilder db = solver.MakePhase(all,
// Solver.INT_VAR_DEFAULT,
// Solver.INT_VAR_SIMPLE,
// Solver.CHOOSE_FIRST_UNBOUND,
// Solver.CHOOSE_RANDOM,
Solver.CHOOSE_MIN_SIZE_LOWEST_MIN,
// Solver.CHOOSE_MIN_SIZE_HIGHEST_MIN,
// Solver.CHOOSE_MIN_SIZE_LOWEST_MAX,
// Solver.CHOOSE_MIN_SIZE_HIGHEST_MAX,
// Solver.CHOOSE_PATH,
// Solver.INT_VALUE_DEFAULT
// Solver.INT_VALUE_SIMPLE
// Solver.ASSIGN_MIN_VALUE
// Solver.ASSIGN_MAX_VALUE
Solver.ASSIGN_RANDOM_VALUE
// Solver.ASSIGN_CENTER_VALUE
);
/*
* DefaultPhaseParameters parameters = new DefaultPhaseParameters();
*
* parameters.heuristic_period = 20;
* // parameters.heuristic_period = 10;
*
* // parameters.heuristic_num_failures_limit = 1000;
* // parameters.restart_log_size = -1;
* // parameters.restart_log_size = 100;
* // parameters.run_all_heuristics = false;
*
* // parameters.var_selection_schema = DefaultPhaseParameters.CHOOSE_MAX_SUM_IMPACT;
* // parameters.var_selection_schema = DefaultPhaseParameters.CHOOSE_MAX_AVERAGE_IMPACT ;
* parameters.var_selection_schema = DefaultPhaseParameters.CHOOSE_MAX_VALUE_IMPACT;
*
* // parameters.value_selection_schema = DefaultPhaseParameters.SELECT_MIN_IMPACT;
* parameters.value_selection_schema = DefaultPhaseParameters.SELECT_MAX_IMPACT;
*
* parameters.initialization_splits = 10;
* // parameters.initialization_splits = 20;
* // parameters.initialization_splits = n;
*
* // parameters.random_seed = 0;
*
* DecisionBuilder db = solver.MakeDefaultPhase(all, parameters);
*/
OptimizeVar opt = diff.Minimize(1);
solver.NewSearch(db, opt);
int sols = 0;
while (solver.NextSolution())
{
sols++;
Console.WriteLine("\n\nDiff: " + diff.Value());
if (n <= 10000)
{
Console.WriteLine("Assignment: ");
long[] assignments = new long[n];
foreach (int i in NRange)
{
Console.Write(x[i].Value() + " ");
assignments[i] = x[i].Value();
}
Console.WriteLine();
for (int team = 0; team < num_teams; team++)
{
Console.Write("team " + team + ": ");
foreach (int i in NRange)
{
if (assignments[i] == team)
{
Console.Write((i) + " ");
}
}
Console.WriteLine();
}
}
Console.Write("Sum of each team : ");
for (int t = 0; t < num_teams; t++)
{
Console.Write(team_sum[t].Value() + " ");
}
Console.WriteLine();
Console.Write("Number of members in each team: ");
for (int t = 0; t < num_teams; t++)
{
Console.Write(team_num[t].Value() + " ");
}
Console.WriteLine();
if (sols_to_show > 0 && sols >= sols_to_show)
{
break;
}
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Moving furnitures (scheduling) problem in Google CP Solver.
*
* Marriott & Stukey: 'Programming with constraints', page 112f
*
* The model implements an decomposition of the global constraint
* cumulative (see above).
*
* Also see http://www.hakank.org/or-tools/furniture_moving.py
*
*/
private static void Solve()
{
Solver solver = new Solver("FurnitureMoving");
int n = 4;
int[] duration = { 30, 10, 15, 15 };
int[] demand = { 3, 1, 3, 2 };
int upper_limit = 160;
//
// Decision variables
//
IntVar[] start_times = solver.MakeIntVarArray(n, 0, upper_limit, "start_times");
IntVar[] end_times = solver.MakeIntVarArray(n, 0, upper_limit * 2, "end_times");
IntVar end_time = solver.MakeIntVar(0, upper_limit * 2, "end_time");
// number of needed resources, to be minimized or constrained
IntVar num_resources = solver.MakeIntVar(0, 10, "num_resources");
//
// Constraints
//
for (int i = 0; i < n; i++)
{
solver.Add(end_times[i] == start_times[i] + duration[i]);
}
solver.Add(end_time == end_times.Max());
MyCumulative(solver, start_times, duration, demand, num_resources);
//
// Some extra constraints to play with
//
// all tasks must end within an hour
// solver.Add(end_time <= 60);
// All tasks should start at time 0
// for(int i = 0; i < n; i++) {
// solver.Add(start_times[i] == 0);
// }
// limitation of the number of people
// solver.Add(num_resources <= 3);
solver.Add(num_resources <= 4);
//
// Objective
//
// OptimizeVar obj = num_resources.Minimize(1);
OptimizeVar obj = end_time.Minimize(1);
//
// Search
//
DecisionBuilder db =
solver.MakePhase(start_times, Solver.CHOOSE_FIRST_UNBOUND, Solver.ASSIGN_MIN_VALUE);
solver.NewSearch(db, obj);
while (solver.NextSolution())
{
Console.WriteLine("num_resources: {0} end_time: {1}", num_resources.Value(),
end_time.Value());
for (int i = 0; i < n; i++)
{
Console.WriteLine("Task {0,1}: {1,2} -> {2,2} -> {3,2} (demand: {4})", i,
start_times[i].Value(), duration[i], end_times[i].Value(), demand[i]);
}
Console.WriteLine();
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
public void EndSearch()
{
pinned_decision_builder_ = null;
pinned_search_monitors_.Clear();
EndSearchAux();
}
/**
*
* Bus scheduling.
*
* Minimize number of buses in timeslots.
*
* Problem from Taha "Introduction to Operations Research", page 58.
*
* This is a slightly more general model than Taha's.
*
* Also see, http://www.hakank.org/or-tools/bus_schedule.py
*
*/
private static long Solve(long num_buses_check = 0)
{
Solver solver = new Solver("BusSchedule");
//
// data
//
int time_slots = 6;
int[] demands = { 8, 10, 7, 12, 4, 4 };
int max_num = demands.Sum();
//
// Decision variables
//
// How many buses start the schedule at time slot t
IntVar[] x = solver.MakeIntVarArray(time_slots, 0, max_num, "x");
// Total number of buses
IntVar num_buses = x.Sum().VarWithName("num_buses");
//
// Constraints
//
// Meet the demands for this and the next time slot.
for (int i = 0; i < time_slots - 1; i++)
{
solver.Add(x[i] + x[i + 1] >= demands[i]);
}
// The demand "around the clock"
solver.Add(x[time_slots - 1] + x[0] - demands[time_slots - 1] == 0);
// For showing all solutions of minimal number of buses
if (num_buses_check > 0)
{
solver.Add(num_buses == num_buses_check);
}
//
// Search
//
DecisionBuilder db = solver.MakePhase(x, Solver.CHOOSE_FIRST_UNBOUND, Solver.ASSIGN_MIN_VALUE);
if (num_buses_check == 0)
{
// Minimize num_buses
OptimizeVar obj = num_buses.Minimize(1);
solver.NewSearch(db, obj);
}
else
{
solver.NewSearch(db);
}
long result = 0;
while (solver.NextSolution())
{
result = num_buses.Value();
Console.Write("x: ");
for (int i = 0; i < time_slots; i++)
{
Console.Write("{0,2} ", x[i].Value());
}
Console.WriteLine("num_buses: " + num_buses.Value());
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
return(result);
}
/**
*
* Nurse rostering
*
* This is a simple nurse rostering model using a DFA and
* my decomposition of regular constraint.
*
* The DFA is from MiniZinc Tutorial, Nurse Rostering example:
* - one day off every 4 days
* - no 3 nights in a row.
*
* Also see http://www.hakank.org/or-tools/nurse_rostering.py
*
*/
private static void Solve()
{
Solver solver = new Solver("NurseRostering");
//
// Data
//
// Note: If you change num_nurses or num_days,
// please also change the constraints
// on nurse_stat and/or day_stat.
int num_nurses = 7;
int num_days = 14;
// Note: I had to add a dummy shift.
int dummy_shift = 0;
int day_shift = 1;
int night_shift = 2;
int off_shift = 3;
int[] shifts = { dummy_shift, day_shift, night_shift, off_shift };
int[] valid_shifts = { day_shift, night_shift, off_shift };
// the DFA (for regular)
int n_states = 6;
int input_max = 3;
int initial_state = 1; // 0 is for the failing state
int[] accepting_states = { 1, 2, 3, 4, 5, 6 };
int[,] transition_fn =
{
// d,n,o
{ 2, 3, 1 }, // state 1
{ 4, 4, 1 }, // state 2
{ 4, 5, 1 }, // state 3
{ 6, 6, 1 }, // state 4
{ 6, 0, 1 }, // state 5
{ 0, 0, 1 } // state 6
};
string[] days = { "d", "n", "o" }; // for presentation
//
// Decision variables
//
// For regular
IntVar[,] x =
solver.MakeIntVarMatrix(num_nurses, num_days, valid_shifts, "x");
IntVar[] x_flat = x.Flatten();
// summary of the nurses
IntVar[] nurse_stat =
solver.MakeIntVarArray(num_nurses, 0, num_days, "nurse_stat");
// summary of the shifts per day
int num_shifts = shifts.Length;
IntVar[,] day_stat = new IntVar[num_days, num_shifts];
for (int i = 0; i < num_days; i++)
{
for (int j = 0; j < num_shifts; j++)
{
day_stat[i, j] = solver.MakeIntVar(0, num_nurses, "day_stat");
}
}
//
// Constraints
//
for (int i = 0; i < num_nurses; i++)
{
IntVar[] reg_input = new IntVar[num_days];
for (int j = 0; j < num_days; j++)
{
reg_input[j] = x[i, j];
}
MyRegular(solver, reg_input, n_states, input_max, transition_fn,
initial_state, accepting_states);
}
//
// Statistics and constraints for each nurse
//
for (int i = 0; i < num_nurses; i++)
{
// Number of worked days (either day or night shift)
IntVar[] b = new IntVar[num_days];
for (int j = 0; j < num_days; j++)
{
b[j] = ((x[i, j] == day_shift) + (x[i, j] == night_shift)).Var();
}
solver.Add(b.Sum() == nurse_stat[i]);
// Each nurse must work between 7 and 10
// days/nights during this period
solver.Add(nurse_stat[i] >= 7);
solver.Add(nurse_stat[i] <= 10);
}
//
// Statistics and constraints for each day
//
for (int j = 0; j < num_days; j++)
{
for (int t = 0; t < num_shifts; t++)
{
IntVar[] b = new IntVar[num_nurses];
for (int i = 0; i < num_nurses; i++)
{
b[i] = x[i, j] == t;
}
solver.Add(b.Sum() == day_stat[j, t]);
}
//
// Some constraints for each day:
//
// Note: We have a strict requirements of
// the number of shifts.
// Using atleast constraints is harder
// in this model.
//
if (j % 7 == 5 || j % 7 == 6)
{
// special constraints for the weekends
solver.Add(day_stat[j, day_shift] == 2);
solver.Add(day_stat[j, night_shift] == 1);
solver.Add(day_stat[j, off_shift] == 4);
}
else
{
// for workdays:
// - exactly 3 on day shift
solver.Add(day_stat[j, day_shift] == 3);
// - exactly 2 on night
solver.Add(day_stat[j, night_shift] == 2);
// - exactly 2 off duty
solver.Add(day_stat[j, off_shift] == 2);
}
}
//
// Search
//
DecisionBuilder db = solver.MakePhase(x_flat,
Solver.CHOOSE_FIRST_UNBOUND,
Solver.ASSIGN_MIN_VALUE);
solver.NewSearch(db);
int num_solutions = 0;
while (solver.NextSolution())
{
num_solutions++;
for (int i = 0; i < num_nurses; i++)
{
Console.Write("Nurse #{0,-2}: ", i);
var occ = new Dictionary <int, int>();
for (int j = 0; j < num_days; j++)
{
int v = (int)x[i, j].Value() - 1;
if (!occ.ContainsKey(v))
{
occ[v] = 0;
}
occ[v]++;
Console.Write(days[v] + " ");
}
Console.Write(" #workdays: {0,2}", nurse_stat[i].Value());
foreach (int s in valid_shifts)
{
int v = 0;
if (occ.ContainsKey(s - 1))
{
v = occ[s - 1];
}
Console.Write(" {0}:{1}", days[s - 1], v);
}
Console.WriteLine();
}
Console.WriteLine();
Console.WriteLine("Statistics per day:\nDay d n o");
for (int j = 0; j < num_days; j++)
{
Console.Write("Day #{0,2}: ", j);
foreach (int t in valid_shifts)
{
Console.Write(day_stat[j, t].Value() + " ");
}
Console.WriteLine();
}
Console.WriteLine();
// We just show 2 solutions
if (num_solutions > 1)
{
break;
}
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
public void NewSearch(DecisionBuilder db,
SearchMonitor sm1,
SearchMonitor sm2,
SearchMonitor sm3,
SearchMonitor sm4) {
pinned_decision_builder_ = db;
pinned_search_monitors_.Clear();
pinned_search_monitors_.Add(sm1);
pinned_search_monitors_.Add(sm2);
pinned_search_monitors_.Add(sm3);
pinned_search_monitors_.Add(sm4);
NewSearchAux(db, sm1, sm2, sm3, sm4);
}
/**
*
* Set partition problem.
*
* Problem formulation from
* http://www.koalog.com/resources/samples/PartitionProblem.java.html
* """
* This is a partition problem.
* Given the set S = {1, 2, ..., n},
* it consists in finding two sets A and B such that:
*
* A U B = S,
* |A| = |B|,
* sum(A) = sum(B),
* sum_squares(A) = sum_squares(B)
*
* """
*
* This model uses a binary matrix to represent the sets.
*
*
* Also see http://www.hakank.org/or-tools/set_partition.py
*
*/
private static void Solve(int n = 16, int num_sets = 2)
{
Solver solver = new Solver("SetPartition");
Console.WriteLine("n: {0}", n);
Console.WriteLine("num_sets: {0}", num_sets);
IEnumerable <int> Sets = Enumerable.Range(0, num_sets);
IEnumerable <int> NRange = Enumerable.Range(0, n);
//
// Decision variables
//
IntVar[,] a = solver.MakeIntVarMatrix(num_sets, n, 0, 1, "a");
IntVar[] a_flat = a.Flatten();
//
// Constraints
//
// partition set
partition_sets(solver, a, num_sets, n);
foreach (int i in Sets)
{
foreach (int j in Sets)
{
// same cardinality
solver.Add((from k in NRange select a[i, k]).ToArray().Sum() ==
(from k in NRange select a[j, k]).ToArray().Sum());
// same sum
solver.Add((from k in NRange select(k * a[i, k])).ToArray().Sum() ==
(from k in NRange select(k * a[j, k])).ToArray().Sum());
// same sum squared
solver.Add((from k in NRange select(k * a[i, k] * k * a[i, k])).ToArray().Sum() ==
(from k in NRange select(k * a[j, k] * k * a[j, k])).ToArray().Sum());
}
}
// symmetry breaking for num_sets == 2
if (num_sets == 2)
{
solver.Add(a[0, 0] == 1);
}
//
// Search
//
DecisionBuilder db = solver.MakePhase(a_flat, Solver.INT_VAR_DEFAULT, Solver.INT_VALUE_DEFAULT);
solver.NewSearch(db);
while (solver.NextSolution())
{
int[,] a_val = new int[num_sets, n];
foreach (int i in Sets)
{
foreach (int j in NRange)
{
a_val[i, j] = (int)a[i, j].Value();
}
}
Console.WriteLine("sums: {0}",
(from j in NRange select(j + 1) * a_val[0, j]).ToArray().Sum());
Console.WriteLine(
"sums squared: {0}",
(from j in NRange select(int) Math.Pow((j + 1) * a_val[0, j], 2)).ToArray().Sum());
// Show the numbers in each set
foreach (int i in Sets)
{
if ((from j in NRange select a_val[i, j]).ToArray().Sum() > 0)
{
Console.Write(i + 1 + ": ");
foreach (int j in NRange)
{
if (a_val[i, j] == 1)
{
Console.Write((j + 1) + " ");
}
}
Console.WriteLine();
}
}
Console.WriteLine();
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Secret Santa problem II in Google CP Solver.
*
* From Maple Primes: 'Secret Santa Graph Theory'
* http://www.mapleprimes.com/blog/jpmay/secretsantagraphtheory
* """
* Every year my extended family does a 'secret santa' gift exchange.
* Each person draws another person at random and then gets a gift for
* them. At first, none of my siblings were married, and so the draw was
* completely random. Then, as people got married, we added the restriction
* that spouses should not draw each others names. This restriction meant
* that we moved from using slips of paper on a hat to using a simple
* computer program to choose names. Then people began to complain when
* they would get the same person two years in a row, so the program was
* modified to keep some history and avoid giving anyone a name in their
* recent history. This year, not everyone was participating, and so after
* removing names, and limiting the number of exclusions to four per person,
* I had data something like this:
*
* Name: Spouse, Recent Picks
*
* Noah: Ava. Ella, Evan, Ryan, John
* Ava: Noah, Evan, Mia, John, Ryan
* Ryan: Mia, Ella, Ava, Lily, Evan
* Mia: Ryan, Ava, Ella, Lily, Evan
* Ella: John, Lily, Evan, Mia, Ava
* John: Ella, Noah, Lily, Ryan, Ava
* Lily: Evan, John, Mia, Ava, Ella
* Evan: Lily, Mia, John, Ryan, Noah
* """
*
* Note: I interpret this as the following three constraints:
* 1) One cannot be a Secret Santa of one's spouse
* 2) One cannot be a Secret Santa for somebody two years in a row
* 3) Optimization: maximize the time since the last time
*
* This model also handle single persons, something the original
* problem don't mention.
*
*
* Also see http://www.hakank.org/or-tools/secret_santa2.py
*
*/
private static void Solve(int single = 0)
{
Solver solver = new Solver("SecretSanta2");
Console.WriteLine("\nSingle: {0}", single);
//
// The matrix version of earlier rounds.
// M means that no earlier Santa has been assigned.
// Note: Ryan and Mia has the same recipient for years 3 and 4,
// and Ella and John has for year 4.
// This seems to be caused by modification of
// original data.
//
int n_no_single = 8;
int M = n_no_single + 1;
int[][] rounds_no_single =
{
// N A R M El J L Ev
new int[] { 0, M, 3, M, 1, 4, M, 2 }, // Noah
new int[] { M, 0, 4, 2, M, 3, M, 1 }, // Ava
new int[] { M, 2, 0, M, 1, M, 3, 4 }, // Ryan
new int[] { M, 1, M, 0, 2, M, 3, 4 }, // Mia
new int[] { M, 4, M, 3, 0, M, 1, 2 }, // Ella
new int[] { 1, 4, 3, M, M, 0, 2, M }, // John
new int[] { M, 3, M, 2, 4, 1, 0, M }, // Lily
new int[] { 4, M, 3, 1, M, 2, M, 0 } // Evan
};
//
// Rounds with a single person (fake data)
//
int n_with_single = 9;
M = n_with_single + 1;
int[][] rounds_single =
{
// N A R M El J L Ev S
new int[] { 0, M, 3, M, 1, 4, M, 2, 2 }, // Noah
new int[] { M, 0, 4, 2, M, 3, M, 1, 1 }, // Ava
new int[] { M, 2, 0, M, 1, M, 3, 4, 4 }, // Ryan
new int[] { M, 1, M, 0, 2, M, 3, 4, 3 }, // Mia
new int[] { M, 4, M, 3, 0, M, 1, 2, M }, // Ella
new int[] { 1, 4, 3, M, M, 0, 2, M, M }, // John
new int[] { M, 3, M, 2, 4, 1, 0, M, M }, // Lily
new int[] { 4, M, 3, 1, M, 2, M, 0, M }, // Evan
new int[] { 1, 2, 3, 4, M, 2, M, M, 0 } // Single
};
int Noah = 0;
int Ava = 1;
int Ryan = 2;
int Mia = 3;
int Ella = 4;
int John = 5;
int Lily = 6;
int Evan = 7;
int n = n_no_single;
int[][] rounds = rounds_no_single;
if (single == 1)
{
n = n_with_single;
rounds = rounds_single;
}
M = n + 1;
IEnumerable <int> RANGE = Enumerable.Range(0, n);
String[] persons = { "Noah", "Ava", "Ryan", "Mia", "Ella",
"John", "Lily", "Evan", "Single" };
int[] spouses =
{
Ava, // Noah
Noah, // Ava
Mia, // Rya
Ryan, // Mia
John, // Ella
Ella, // John
Evan, // Lily
Lily, // Evan
-1 // Single has no spouse
};
//
// Decision variables
//
IntVar[] santas = solver.MakeIntVarArray(n, 0, n - 1, "santas");
IntVar[] santa_distance = solver.MakeIntVarArray(n, 0, M, "santa_distance");
// total of "distance", to maximize
IntVar z = santa_distance.Sum().VarWithName("z");
//
// Constraints
//
solver.Add(santas.AllDifferent());
// Can't be one own"s Secret Santa
// (i.e. ensure that there are no fix-point in the array.)
foreach (int i in RANGE)
{
solver.Add(santas[i] != i);
}
// no Santa for a spouses
foreach (int i in RANGE)
{
if (spouses[i] > -1)
{
solver.Add(santas[i] != spouses[i]);
}
}
// optimize "distance" to earlier rounds:
foreach (int i in RANGE)
{
solver.Add(santa_distance[i] == rounds[i].Element(santas[i]));
}
// cannot be a Secret Santa for the same person
// two years in a row.
foreach (int i in RANGE)
{
foreach (int j in RANGE)
{
if (rounds[i][j] == 1)
{
solver.Add(santas[i] != j);
}
}
}
//
// Objective (minimize the distances)
//
OptimizeVar obj = z.Maximize(1);
//
// Search
//
DecisionBuilder db = solver.MakePhase(santas,
Solver.CHOOSE_MIN_SIZE_LOWEST_MIN,
Solver.ASSIGN_CENTER_VALUE);
solver.NewSearch(db, obj);
while (solver.NextSolution())
{
Console.WriteLine("\ntotal distances: {0}", z.Value());
Console.Write("santas: ");
for (int i = 0; i < n; i++)
{
Console.Write(santas[i].Value() + " ");
}
Console.WriteLine();
foreach (int i in RANGE)
{
Console.WriteLine("{0}\tis a Santa to {1} (distance {2})",
persons[i],
persons[santas[i].Value()],
santa_distance[i].Value());
}
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* From "God plays dice"
* "A puzzle"
* http://gottwurfelt.wordpress.com/2012/02/22/a-puzzle/
* And the sequel "Answer to a puzzle"
* http://gottwurfelt.wordpress.com/2012/02/24/an-answer-to-a-puzzle/
*
* This problem instance was taken from the latter blog post.
* (Problem 1)
*
* """
* 8809 = 6
* 7111 = 0
* 2172 = 0
* 6666 = 4
* 1111 = 0
* 3213 = 0
* 7662 = 2
* 9312 = 1
* 0000 = 4
* 2222 = 0
* 3333 = 0
* 5555 = 0
* 8193 = 3
* 8096 = 5
* 7777 = 0
* 9999 = 4
* 7756 = 1
* 6855 = 3
* 9881 = 5
* 5531 = 0
*
* 2581 = ?
* """
*
* Note:
* This model yields 10 solutions, since x4 is not
* restricted in the constraints.
* All solutions has x assigned to the correct result.
*
*
* (Problem 2)
* The problem stated in "A puzzle"
* http://gottwurfelt.wordpress.com/2012/02/22/a-puzzle/
* is
* """
* 8809 = 6
* 7662 = 2
* 9312 = 1
* 8193 = 3
* 8096 = 5
* 7756 = 1
* 6855 = 3
* 9881 = 5
*
* 2581 = ?
* """
* This problem instance yields two different solutions of x,
* one is the same (correct) as for the above problem instance,
* and one is not.
* This is because here x0,x1,x4 and x9 are underdefined.
*
*
*/
private static void Solve(int p = 1)
{
Solver solver = new Solver("APuzzle");
Console.WriteLine("\nSolving p{0}", p);
//
// Data
//
int n = 10;
//
// Decision variables
//
IntVar x0 = solver.MakeIntVar(0, n - 1, "x0");
IntVar x1 = solver.MakeIntVar(0, n - 1, "x1");
IntVar x2 = solver.MakeIntVar(0, n - 1, "x2");
IntVar x3 = solver.MakeIntVar(0, n - 1, "x3");
IntVar x4 = solver.MakeIntVar(0, n - 1, "x4");
IntVar x5 = solver.MakeIntVar(0, n - 1, "x5");
IntVar x6 = solver.MakeIntVar(0, n - 1, "x6");
IntVar x7 = solver.MakeIntVar(0, n - 1, "x7");
IntVar x8 = solver.MakeIntVar(0, n - 1, "x8");
IntVar x9 = solver.MakeIntVar(0, n - 1, "x9");
IntVar[] all = { x0, x1, x2, x3, x4, x5, x6, x7, x8, x9 };
// The unknown, i.e. 2581 = x
IntVar x = solver.MakeIntVar(0, n - 1, "x");
//
// Constraints
//
// Both problem are here shown in two
// approaches:
// - using equations
// - using a a matrix and Sum of each row
if (p == 1)
{
// Problem 1
solver.Add(x8 + x8 + x0 + x9 == 6);
solver.Add(x7 + x1 + x1 + x1 == 0);
solver.Add(x2 + x1 + x7 + x2 == 0);
solver.Add(x6 + x6 + x6 + x6 == 4);
solver.Add(x1 + x1 + x1 + x1 == 0);
solver.Add(x3 + x2 + x1 + x3 == 0);
solver.Add(x7 + x6 + x6 + x2 == 2);
solver.Add(x9 + x3 + x1 + x2 == 1);
solver.Add(x0 + x0 + x0 + x0 == 4);
solver.Add(x2 + x2 + x2 + x2 == 0);
solver.Add(x3 + x3 + x3 + x3 == 0);
solver.Add(x5 + x5 + x5 + x5 == 0);
solver.Add(x8 + x1 + x9 + x3 == 3);
solver.Add(x8 + x0 + x9 + x6 == 5);
solver.Add(x7 + x7 + x7 + x7 == 0);
solver.Add(x9 + x9 + x9 + x9 == 4);
solver.Add(x7 + x7 + x5 + x6 == 1);
solver.Add(x6 + x8 + x5 + x5 == 3);
solver.Add(x9 + x8 + x8 + x1 == 5);
solver.Add(x5 + x5 + x3 + x1 == 0);
// The unknown
solver.Add(x2 + x5 + x8 + x1 == x);
}
else if (p == 2)
{
// Another representation of Problem 1
int[,] problem1 = { { 8, 8, 0, 9, 6 }, { 7, 1, 1, 1, 0 }, { 2, 1, 7, 2, 0 },
{ 6, 6, 6, 6, 4 }, { 1, 1, 1, 1, 0 }, { 3, 2, 1, 3, 0 },
{ 7, 6, 6, 2, 2 }, { 9, 3, 1, 2, 1 }, { 0, 0, 0, 0, 4 },
{ 2, 2, 2, 2, 0 }, { 3, 3, 3, 3, 0 }, { 5, 5, 5, 5, 0 },
{ 8, 1, 9, 3, 3 }, { 8, 0, 9, 6, 5 }, { 7, 7, 7, 7, 0 },
{ 9, 9, 9, 9, 4 }, { 7, 7, 5, 6, 1 }, { 6, 8, 5, 5, 3 },
{ 9, 8, 8, 1, 5 }, { 5, 5, 3, 1, 0 } };
for (int i = 0; i < problem1.GetLength(0); i++)
{
solver.Add((from j in Enumerable.Range(0, 4) select all[problem1[i, j]]).ToArray().Sum() ==
problem1[i, 4]);
}
solver.Add(all[2] + all[5] + all[8] + all[1] == x);
}
else if (p == 3)
{
// Problem 2
solver.Add(x8 + x8 + x0 + x9 == 6);
solver.Add(x7 + x6 + x6 + x2 == 2);
solver.Add(x9 + x3 + x1 + x2 == 1);
solver.Add(x8 + x1 + x9 + x3 == 3);
solver.Add(x8 + x0 + x9 + x6 == 5);
solver.Add(x7 + x7 + x5 + x6 == 1);
solver.Add(x6 + x8 + x5 + x5 == 3);
solver.Add(x9 + x8 + x8 + x1 == 5);
// The unknown
solver.Add(x2 + x5 + x8 + x1 == x);
}
else
{
// Another representation of Problem 2
int[,] problem2 = { { 8, 8, 0, 9, 6 }, { 7, 6, 6, 2, 2 }, { 9, 3, 1, 2, 1 },
{ 8, 1, 9, 3, 3 }, { 8, 0, 9, 6, 5 }, { 7, 7, 5, 6, 1 },
{ 6, 8, 5, 5, 3 }, { 9, 8, 8, 1, 5 } };
for (int i = 0; i < problem2.GetLength(0); i++)
{
solver.Add((from j in Enumerable.Range(0, 4) select all[problem2[i, j]]).ToArray().Sum() ==
problem2[i, 4]);
}
solver.Add(all[2] + all[5] + all[8] + all[1] == x);
}
//
// Search
//
DecisionBuilder db = solver.MakePhase(all, Solver.INT_VAR_DEFAULT, Solver.INT_VALUE_DEFAULT);
solver.NewSearch(db);
while (solver.NextSolution())
{
Console.Write("x: {0} x0..x9: ", x.Value());
for (int i = 0; i < n; i++)
{
Console.Write(all[i].Value() + " ");
}
Console.WriteLine();
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Ski assignment in Google CP Solver.
*
* From Jeffrey Lee Hellrung, Jr.:
* PIC 60, Fall 2008 Final Review, December 12, 2008
* http://www.math.ucla.edu/~jhellrun/course_files/Fall%25202008/PIC%252060%2520-%2520Data%2520Structures%2520and%2520Algorithms/final_review.pdf
* """
* 5. Ski Optimization! Your job at Snapple is pleasant but in the winter
* you've decided to become a ski bum. You've hooked up with the Mount
* Baldy Ski Resort. They'll let you ski all winter for free in exchange
* for helping their ski rental shop with an algorithm to assign skis to
* skiers. Ideally, each skier should obtain a pair of skis whose height
* matches his or her own height exactly. Unfortunately, this is generally
* not possible. We define the disparity between a skier and his or her
* skis to be the absolute value of the difference between the height of
* the skier and the pair of skis. Our objective is to find an assignment
* of skis to skiers that minimizes the sum of the disparities.
* ...
* Illustrate your algorithm by explicitly filling out the A[i, j] table
* for the following sample data:
* - Ski heights : 1, 2, 5, 7, 13, 21.
* - Skier heights: 3, 4, 7, 11, 18.
* """
*
* Also see http://www.hakank.org/or-tools/ski_assignment.py
*
*
*/
private static void Solve()
{
Solver solver = new Solver("SkiAssignment");
//
// Data
//
int num_skis = 6;
int num_skiers = 5;
int[] ski_heights = { 1, 2, 5, 7, 13, 21 };
int[] skier_heights = { 3, 4, 7, 11, 18 };
//
// Decision variables
//
IntVar[] x = solver.MakeIntVarArray(num_skiers, 0, num_skis - 1, "x");
//
// Constraints
//
solver.Add(x.AllDifferent());
IntVar[] z_tmp = new IntVar[num_skiers];
for (int i = 0; i < num_skiers; i++)
{
z_tmp[i] = (ski_heights.Element(x[i]) - skier_heights[i]).Abs().Var();
}
// IntVar z = solver.MakeIntVar(0, ski_heights.Sum(), "z");
// solver.Add(z_tmp.Sum() == z);
// The direct cast from IntExpr to IntVar is potentially faster than
// the above code.
IntVar z = z_tmp.Sum().VarWithName("z");
//
// Objective
//
OptimizeVar obj = z.Minimize(1);
//
// Search
//
DecisionBuilder db = solver.MakePhase(x, Solver.CHOOSE_FIRST_UNBOUND, Solver.INT_VALUE_DEFAULT);
solver.NewSearch(db, obj);
while (solver.NextSolution())
{
Console.Write("z: {0} x: ", z.Value());
for (int i = 0; i < num_skiers; i++)
{
Console.Write(x[i].Value() + " ");
}
Console.WriteLine();
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Crypto problem.
*
* This is the standard benchmark "crypto" problem.
*
* From GLPK:s model cryto.mod.
*
* """
* This problem comes from the newsgroup rec.puzzle.
* The numbers from 1 to 26 are assigned to the letters of the alphabet.
* The numbers beside each word are the total of the values assigned to
* the letters in the word (e.g. for LYRE: L, Y, R, E might be to equal
* 5, 9, 20 and 13, or any other combination that add up to 47).
* Find the value of each letter under the equations:
*
* BALLET 45 GLEE 66 POLKA 59 SONG 61
* CELLO 43 JAZZ 58 QUARTET 50 SOPRANO 82
* CONCERT 74 LYRE 47 SAXOPHONE 134 THEME 72
* FLUTE 30 OBOE 53 SCALE 51 VIOLIN 100
* FUGUE 50 OPERA 65 SOLO 37 WALTZ 34
*
* Solution:
* A, B,C, D, E,F, G, H, I, J, K,L,M, N, O, P,Q, R, S,T,U, V,W, X, Y, Z
* 5,13,9,16,20,4,24,21,25,17,23,2,8,12,10,19,7,11,15,3,1,26,6,22,14,18
*
* Reference:
* Koalog Constraint Solver <http://www.koalog.com/php/jcs.php>,
* Simple problems, the crypto-arithmetic puzzle ALPHACIPHER.
* """
*
* Also see http://hakank.org/or-tools/crypto.py
*
*/
private static void Solve()
{
Solver solver = new Solver("Crypto");
int num_letters = 26;
int BALLET = 45;
int CELLO = 43;
int CONCERT = 74;
int FLUTE = 30;
int FUGUE = 50;
int GLEE = 66;
int JAZZ = 58;
int LYRE = 47;
int OBOE = 53;
int OPERA = 65;
int POLKA = 59;
int QUARTET = 50;
int SAXOPHONE = 134;
int SCALE = 51;
int SOLO = 37;
int SONG = 61;
int SOPRANO = 82;
int THEME = 72;
int VIOLIN = 100;
int WALTZ = 34;
//
// Decision variables
//
IntVar[] LD = solver.MakeIntVarArray(num_letters, 1, num_letters, "LD");
// Note D is not used in the constraints below
IntVar A = LD[0]; IntVar B = LD[1]; IntVar C = LD[2]; // IntVar D = LD[3];
IntVar E = LD[4]; IntVar F = LD[5]; IntVar G = LD[6]; IntVar H = LD[7];
IntVar I = LD[8]; IntVar J = LD[9]; IntVar K = LD[10]; IntVar L = LD[11];
IntVar M = LD[12]; IntVar N = LD[13]; IntVar O = LD[14]; IntVar P = LD[15];
IntVar Q = LD[16]; IntVar R = LD[17]; IntVar S = LD[18]; IntVar T = LD[19];
IntVar U = LD[20]; IntVar V = LD[21]; IntVar W = LD[22]; IntVar X = LD[23];
IntVar Y = LD[24]; IntVar Z = LD[25];
//
// Constraints
//
solver.Add(LD.AllDifferent());
solver.Add(B + A + L + L + E + T == BALLET);
solver.Add(C + E + L + L + O == CELLO);
solver.Add(C + O + N + C + E + R + T == CONCERT);
solver.Add(F + L + U + T + E == FLUTE);
solver.Add(F + U + G + U + E == FUGUE);
solver.Add(G + L + E + E == GLEE);
solver.Add(J + A + Z + Z == JAZZ);
solver.Add(L + Y + R + E == LYRE);
solver.Add(O + B + O + E == OBOE);
solver.Add(O + P + E + R + A == OPERA);
solver.Add(P + O + L + K + A == POLKA);
solver.Add(Q + U + A + R + T + E + T == QUARTET);
solver.Add(S + A + X + O + P + H + O + N + E == SAXOPHONE);
solver.Add(S + C + A + L + E == SCALE);
solver.Add(S + O + L + O == SOLO);
solver.Add(S + O + N + G == SONG);
solver.Add(S + O + P + R + A + N + O == SOPRANO);
solver.Add(T + H + E + M + E == THEME);
solver.Add(V + I + O + L + I + N == VIOLIN);
solver.Add(W + A + L + T + Z == WALTZ);
//
// Search
//
DecisionBuilder db = solver.MakePhase(LD,
Solver.CHOOSE_MIN_SIZE_LOWEST_MIN,
Solver.ASSIGN_CENTER_VALUE);
solver.NewSearch(db);
String str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ";
while (solver.NextSolution())
{
for (int i = 0; i < num_letters; i++)
{
Console.WriteLine("{0}: {1,2}", str[i], LD[i].Value());
}
Console.WriteLine();
}
Console.WriteLine("\nWallTime: " + solver.WallTime() + "ms ");
Console.WriteLine("Failures: " + solver.Failures());
Console.WriteLine("Branches: " + solver.Branches());
solver.EndSearch();
}
/**
*
*
* Organizing a day.
*
* Simple scheduling problem.
*
* Problem formulation from ECLiPSe:
* Slides on (Finite Domain) Constraint Logic Programming, page 38f
* http://eclipse-clp.org/reports/eclipse.ppt
*
*
* Also see http://www.hakank.org/google_or_tools/organize_day.py
*
*/
private static void Solve()
{
Solver solver = new Solver("OrganizeDayIntervals");
int n = 4;
int work = 0;
int mail = 1;
int shop = 2;
int bank = 3;
// the valid times of the day
int begin = 9;
int end = 17;
// tasks
int[] tasks = { work, mail, shop, bank };
// durations
int[] durations = { 4, 1, 2, 1 };
// Arrays for interval variables.
int[] starts_max = { begin, begin, begin, begin };
int[] ends_max = { end - 4, end - 1, end - 2, end - 1 };
// task [i,0] must be finished before task [i,1]
int[,] before_tasks =
{
{ bank, shop },
{ mail, work }
};
//
// Decision variables
//
IntervalVar[] intervals =
solver.MakeFixedDurationIntervalVarArray(n,
starts_max,
ends_max,
durations,
false,
"task");
//
// Constraints
//
DisjunctiveConstraint disjunctive = intervals.Disjunctive("Sequence");
solver.Add(disjunctive);
// specific constraints
for (int t = 0; t < before_tasks.GetLength(0); t++)
{
int before = before_tasks[t, 0];
int after = before_tasks[t, 1];
solver.Add(intervals[after].StartsAfterEnd(intervals[before]));
}
solver.Add(intervals[work].StartsAfter(11));
//
// Search
//
SequenceVar var = disjunctive.SequenceVar();
SequenceVar[] seq_array = new SequenceVar[] { var };
DecisionBuilder db = solver.MakePhase(seq_array, Solver.SEQUENCE_DEFAULT);
solver.NewSearch(db);
while (solver.NextSolution())
{
foreach (int t in tasks)
{
Console.WriteLine(intervals[t].ToString());
}
Console.WriteLine();
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* KenKen puzzle.
*
* http://en.wikipedia.org/wiki/KenKen
* """
* KenKen or KEN-KEN is a style of arithmetic and logical puzzle sharing
* several characteristics with sudoku. The name comes from Japanese and
* is translated as 'square wisdom' or 'cleverness squared'.
* ...
* The objective is to fill the grid in with the digits 1 through 6 such that:
*
* * Each row contains exactly one of each digit
* * Each column contains exactly one of each digit
* * Each bold-outlined group of cells is a cage containing digits which
* achieve the specified result using the specified mathematical operation:
* addition (+),
* subtraction (-),
* multiplication (x),
* and division (/).
* (Unlike in Killer sudoku, digits may repeat within a group.)
*
* ...
* More complex KenKen problems are formed using the principles described
* above but omitting the symbols +, -, x and /, thus leaving them as
* yet another unknown to be determined.
* """
*
* The solution is:
*
* 5 6 3 4 1 2
* 6 1 4 5 2 3
* 4 5 2 3 6 1
* 3 4 1 2 5 6
* 2 3 6 1 4 5
* 1 2 5 6 3 4
*
*
* Also see http://www.hakank.org/or-tools/kenken2.py
* though this C# model has another representation of
* the problem instance.
*
*/
private static void Solve()
{
Solver solver = new Solver("KenKen2");
// size of matrix
int n = 6;
IEnumerable <int> RANGE = Enumerable.Range(0, n);
// For a better view of the problem, see
// http://en.wikipedia.org/wiki/File:KenKenProblem.svg
// hints
// sum, the hints
// Note: this is 1-based
int[][] problem = { new int[] { 11, 1, 1, 2, 1 },
new int[] { 2, 1, 2, 1, 3 },
new int[] { 20, 1, 4, 2, 4 },
new int[] { 6, 1, 5, 1,6, 2, 6, 3, 6 },
new int[] { 3, 2, 2, 2, 3 },
new int[] { 3, 2, 5, 3, 5 },
new int[] { 240, 3, 1, 3,2, 4, 1, 4, 2 },
new int[] { 6, 3, 3, 3, 4 },
new int[] { 6, 4, 3, 5, 3 },
new int[] { 7, 4, 4, 5,4, 5, 5 },
new int[] { 30, 4, 5, 4, 6 },
new int[] { 6, 5, 1, 5, 2 },
new int[] { 9, 5, 6, 6, 6 },
new int[] { 8, 6, 1, 6,2, 6, 3 },
new int[] { 2, 6, 4, 6, 5 } };
int num_p = problem.GetLength(0); // Number of segments
//
// Decision variables
//
IntVar[,] x = solver.MakeIntVarMatrix(n, n, 1, n, "x");
IntVar[] x_flat = x.Flatten();
//
// Constraints
//
//
// alldifferent rows and columns
foreach (int i in RANGE)
{
// rows
solver.Add((from j in RANGE select x[i, j]).ToArray().AllDifferent());
// cols
solver.Add((from j in RANGE select x[j, i]).ToArray().AllDifferent());
}
// Calculate the segments
for (int i = 0; i < num_p; i++)
{
int[] segment = problem[i];
// Remove the sum from the segment
int len = segment.Length - 1;
int[] s2 = new int[len];
Array.Copy(segment, 1, s2, 0, len);
// sum this segment
calc(solver, s2, x, segment[0]);
}
//
// Search
//
DecisionBuilder db = solver.MakePhase(x_flat, Solver.INT_VAR_DEFAULT, Solver.INT_VALUE_DEFAULT);
solver.NewSearch(db);
while (solver.NextSolution())
{
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
Console.Write(x[i, j].Value() + " ");
}
Console.WriteLine();
}
Console.WriteLine();
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Solves a set covering deployment problem.
* See See http://www.hakank.org/or-tools/set_covering_deployment.py
*
*/
private static void Solve()
{
Solver solver = new Solver("SetCoveringDeployment");
//
// data
//
// From http://mathworld.wolfram.com/SetCoveringDeployment.html
string[] countries = { "Alexandria",
"Asia Minor",
"Britain",
"Byzantium",
"Gaul",
"Iberia",
"Rome",
"Tunis" };
int n = countries.Length;
// the incidence matrix (neighbours)
int[,] mat = { { 0, 1, 0, 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 0, 0, 0, 0 },
{ 0, 0, 0, 0, 1, 1, 0, 0 },
{ 1, 1, 0, 0, 0, 0, 1, 0 },
{ 0, 0, 1, 0, 0, 1, 1, 0 },
{ 0, 0, 1, 0, 1, 0, 1, 1 },
{ 1, 0, 0, 1, 1, 1, 0, 1 },
{ 1, 0, 0, 0, 0, 1, 1, 0 } };
//
// Decision variables
//
// First army
IntVar[] x = solver.MakeIntVarArray(n, 0, 1, "x");
// Second (reserve) army
IntVar[] y = solver.MakeIntVarArray(n, 0, 1, "y");
// total number of armies
IntVar num_armies = (x.Sum() + y.Sum()).Var();
//
// Constraints
//
//
// Constraint 1: There is always an army in a city
// (+ maybe a backup)
// Or rather: Is there a backup, there
// must be an an army
//
for (int i = 0; i < n; i++)
{
solver.Add(x[i] >= y[i]);
}
//
// Constraint 2: There should always be an backup
// army near every city
//
for (int i = 0; i < n; i++)
{
IntVar[] count_neighbours = (
from j in Enumerable.Range(0, n)
where mat[i, j] == 1
select(y[j])).ToArray();
solver.Add((x[i] + count_neighbours.Sum()) >= 1);
}
//
// objective
//
OptimizeVar objective = num_armies.Minimize(1);
//
// Search
//
DecisionBuilder db = solver.MakePhase(x,
Solver.INT_VAR_DEFAULT,
Solver.INT_VALUE_DEFAULT);
solver.NewSearch(db, objective);
while (solver.NextSolution())
{
Console.WriteLine("num_armies: " + num_armies.Value());
for (int i = 0; i < n; i++)
{
if (x[i].Value() == 1)
{
Console.Write("Army: " + countries[i] + " ");
}
if (y[i].Value() == 1)
{
Console.WriteLine(" Reverse army: " + countries[i]);
}
}
Console.WriteLine("\n");
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Solves a set covering problem.
* See See http://www.hakank.org/or-tools/set_covering3.py
*
*/
private static void Solve()
{
Solver solver = new Solver("SetCovering3");
//
// data
//
// Set covering problem from
// Katta G. Murty: 'Optimization Models for Decision Making',
// page 302f
// http://ioe.engin.umich.edu/people/fac/books/murty/opti_model/junior-7.pdf
int num_groups = 6;
int num_senators = 10;
// which group does a senator belong to?
int[,] belongs = { { 1, 1, 1, 1, 1, 0, 0, 0, 0, 0 }, // 1 southern
{ 0, 0, 0, 0, 0, 1, 1, 1, 1, 1 }, // 2 northern
{ 0, 1, 1, 0, 0, 0, 0, 1, 1, 1 }, // 3 liberals
{ 1, 0, 0, 0, 1, 1, 1, 0, 0, 0 }, // 4 conservative
{ 0, 0, 1, 1, 1, 1, 1, 0, 1, 0 }, // 5 democrats
{ 1, 1, 0, 0, 0, 0, 0, 1, 0, 1 } }; // 6 republicans
//
// Decision variables
//
IntVar[] x = solver.MakeIntVarArray(num_senators, 0, 1, "x");
// number of assigned senators, to be minimized
IntVar z = x.Sum().Var();
//
// Constraints
//
// ensure that each group is covered by at least
// one senator
for (int i = 0; i < num_groups; i++)
{
IntVar[] b = new IntVar[num_senators];
for (int j = 0; j < num_senators; j++)
{
b[j] = (x[j] * belongs[i, j]).Var();
}
solver.Add(b.Sum() >= 1);
}
//
// objective
//
OptimizeVar objective = z.Minimize(1);
//
// Search
//
DecisionBuilder db = solver.MakePhase(x, Solver.INT_VAR_DEFAULT, Solver.INT_VALUE_DEFAULT);
solver.NewSearch(db, objective);
while (solver.NextSolution())
{
Console.WriteLine("z: " + z.Value());
Console.Write("x: ");
for (int j = 0; j < num_senators; j++)
{
Console.Write(x[j].Value() + " ");
}
Console.WriteLine();
// More details
for (int j = 0; j < num_senators; j++)
{
if (x[j].Value() == 1)
{
Console.Write("Senator " + (1 + j) + " belongs to these groups: ");
for (int i = 0; i < num_groups; i++)
{
if (belongs[i, j] == 1)
{
Console.Write((1 + i) + " ");
}
}
Console.WriteLine();
}
}
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Combinatorial auction.
*
* This is a more general model for the combinatorial example
* in the Numberjack Tutorial, pages 9 and 24 (slides 19/175 and
* 51/175).
*
* See http://www.hakank.org/or-tools/combinatorial_auction2.py
*
* The original and more talkative model is here:
* http://www.hakank.org/numberjack/combinatorial_auction.py
*
*/
private static void Solve()
{
Solver solver = new Solver("CombinatorialAuction2");
//
// Data
//
int n = 5;
// the items for each bid
int[][] items =
{
new int[] { 0, 1 }, // A,B
new int[] { 0, 2 }, // A, C
new int[] { 1, 3 }, // B,D
new int[] { 1,2, 3 }, // B,C,D
new int[] { 0 } // A
};
int[] bid_ids = { 0, 1, 2, 3 };
int[] bid_amount = { 10, 20, 30, 40, 14 };
//
// Decision variables
//
IntVar[] x = solver.MakeIntVarArray(n, 0, 1, "x");
IntVar z = x.ScalProd(bid_amount).VarWithName("z");
//
// Constraints
//
foreach (int bid_id in bid_ids)
{
var tmp2 = (from item in Enumerable.Range(0, n)
from i in Enumerable.Range(0, items[item].Length)
where items [item]
[i] == bid_id select x[item]);
solver.Add(tmp2.ToArray().Sum() <= 1);
}
//
// Objective
//
OptimizeVar obj = z.Maximize(1);
//
// Search
//
DecisionBuilder db = solver.MakePhase(x, Solver.CHOOSE_FIRST_UNBOUND, Solver.ASSIGN_MIN_VALUE);
solver.NewSearch(db, obj);
while (solver.NextSolution())
{
Console.Write("z: {0,2} x: ", z.Value());
for (int i = 0; i < n; i++)
{
Console.Write(x[i].Value() + " ");
}
Console.WriteLine();
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Discrete tomography
*
* Problem from http://eclipse.crosscoreop.com/examples/tomo.ecl.txt
* """
* This is a little 'tomography' problem, taken from an old issue
* of Scientific American.
*
* A matrix which contains zeroes and ones gets "x-rayed" vertically and
* horizontally, giving the total number of ones in each row and column.
* The problem is to reconstruct the contents of the matrix from this
* information. Sample run:
*
* ?- go.
* 0 0 7 1 6 3 4 5 2 7 0 0
* 0
* 0
* 8 * * * * * * * *
* 2 * *
* 6 * * * * * *
* 4 * * * *
* 5 * * * * *
* 3 * * *
* 7 * * * * * * *
* 0
* 0
*
* Eclipse solution by Joachim Schimpf, IC-Parc
* """
*
* See http://www.hakank.org/or-tools/discrete_tomography.py
*
*/
private static void Solve(int[] rowsums, int[] colsums)
{
Solver solver = new Solver("DiscreteTomography");
//
// Data
//
int r = rowsums.Length;
int c = colsums.Length;
Console.Write("rowsums: ");
for (int i = 0; i < r; i++)
{
Console.Write(rowsums[i] + " ");
}
Console.Write("\ncolsums: ");
for (int j = 0; j < c; j++)
{
Console.Write(colsums[j] + " ");
}
Console.WriteLine("\n");
//
// Decision variables
//
IntVar[,] x = solver.MakeIntVarMatrix(r, c, 0, 1, "x");
IntVar[] x_flat = x.Flatten();
//
// Constraints
//
// row sums
for (int i = 0; i < r; i++)
{
var tmp = from j in Enumerable.Range(0, c) select x[i, j];
solver.Add(tmp.ToArray().Sum() == rowsums[i]);
}
// cols sums
for (int j = 0; j < c; j++)
{
var tmp = from i in Enumerable.Range(0, r) select x[i, j];
solver.Add(tmp.ToArray().Sum() == colsums[j]);
}
//
// Search
//
DecisionBuilder db = solver.MakePhase(x_flat,
Solver.CHOOSE_FIRST_UNBOUND,
Solver.ASSIGN_MIN_VALUE);
solver.NewSearch(db);
while (solver.NextSolution())
{
for (int i = 0; i < r; i++)
{
for (int j = 0; j < c; j++)
{
Console.Write("{0} ", x[i, j].Value() == 1 ? "#" : ".");
}
Console.WriteLine();
}
Console.WriteLine();
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Post office problem.
*
* Problem statement:
* http://www-128.ibm.com/developerworks/linux/library/l-glpk2/
*
* From Winston 'Operations Research: Applications and Algorithms':
* """
* A post office requires a different number of full-time employees working
* on different days of the week [summarized below]. Union rules state that
* each full-time employee must work for 5 consecutive days and then receive
* two days off. For example, an employee who works on Monday to Friday
* must be off on Saturday and Sunday. The post office wants to meet its
* daily requirements using only full-time employees. Minimize the number
* of employees that must be hired.
*
* To summarize the important information about the problem:
*
* Every full-time worker works for 5 consecutive days and takes 2 days off
* - Day 1 (Monday): 17 workers needed
* - Day 2 : 13 workers needed
* - Day 3 : 15 workers needed
* - Day 4 : 19 workers needed
* - Day 5 : 14 workers needed
* - Day 6 : 16 workers needed
* - Day 7 (Sunday) : 11 workers needed
*
* The post office needs to minimize the number of employees it needs
* to hire to meet its demand.
* """
*
* Also see http://www.hakank.org/or-tools/post_office_problem2.py
*
*/
private static void Solve()
{
Solver solver = new Solver("PostOfficeProblem2");
//
// Data
//
// days 0..6, monday 0
int n = 7;
int[] need = { 17, 13, 15, 19, 14, 16, 11 };
// Total cost for the 5 day schedule.
// Base cost per day is 100.
// Working saturday is 100 extra
// Working sunday is 200 extra.
int[] cost = { 500, 600, 800, 800, 800, 800, 700 };
//
// Decision variables
//
// No. of workers starting at day i
IntVar[] x = solver.MakeIntVarArray(n, 0, 100, "x");
IntVar total_cost = x.ScalProd(cost).Var();
IntVar num_workers = x.Sum().Var();
//
// Constraints
//
for (int i = 0; i < n; i++)
{
IntVar s = (from j in Enumerable.Range(0, n) where j != (i + 5) % n &&
j != (i + 6) % n select x[j])
.ToArray()
.Sum()
.Var();
solver.Add(s >= need[i]);
}
// Add a limit for the cost
solver.Add(total_cost <= 20000);
//
// objective
//
//
OptimizeVar obj = total_cost.Minimize(100);
//
// Search
//
DecisionBuilder db =
solver.MakePhase(x, Solver.CHOOSE_MIN_SIZE_LOWEST_MIN, Solver.ASSIGN_MIN_VALUE);
solver.NewSearch(db, obj);
while (solver.NextSolution())
{
Console.WriteLine("num_workers: {0}", num_workers.Value());
Console.WriteLine("total_cost: {0}", total_cost.Value());
Console.Write("x: ");
for (int i = 0; i < n; i++)
{
Console.Write(x[i].Value() + " ");
}
Console.WriteLine("\n");
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Assignment problem
*
* From Wayne Winston "Operations Research",
* Assignment Problems, page 393f
* (generalized version with added test column)
*
* See See http://www.hakank.org/or-tools/assignment.py
*
*/
private static void Solve()
{
Solver solver = new Solver("Assignment");
//
// data
//
// Problem instance
// hakank: I added the fifth column to make it more
// interesting
int rows = 4;
int cols = 5;
int[,] cost =
{
{ 14, 5, 8, 7, 15 },
{ 2, 12, 6, 5, 3 },
{ 7, 8, 3, 9, 7 },
{ 2, 4, 6, 10, 1 }
};
//
// Decision variables
//
IntVar[,] x = solver.MakeBoolVarMatrix(rows, cols, "x");
IntVar[] x_flat = x.Flatten();
//
// Constraints
//
// Exacly one assignment per row (task),
// i.e. all rows must be assigned with one worker
for (int i = 0; i < rows; i++)
{
solver.Add((from j in Enumerable.Range(0, cols)
select x[i, j]).ToArray().Sum() == 1);
}
// At most one assignments per column (worker)
for (int j = 0; j < cols; j++)
{
solver.Add((from i in Enumerable.Range(0, rows)
select x[i, j]).ToArray().Sum() <= 1);
}
// Total cost
IntVar total_cost = (from i in Enumerable.Range(0, rows)
from j in Enumerable.Range(0, cols)
select(cost[i, j] * x[i, j])).ToArray().Sum().Var();
//
// objective
//
OptimizeVar objective = total_cost.Minimize(1);
//
// Search
//
DecisionBuilder db = solver.MakePhase(x_flat,
Solver.INT_VAR_DEFAULT,
Solver.INT_VALUE_DEFAULT);
solver.NewSearch(db, objective);
while (solver.NextSolution())
{
Console.WriteLine("total_cost: {0}", total_cost.Value());
for (int i = 0; i < rows; i++)
{
for (int j = 0; j < cols; j++)
{
Console.Write(x[i, j].Value() + " ");
}
Console.WriteLine();
}
Console.WriteLine();
Console.WriteLine("Assignments:");
for (int i = 0; i < rows; i++)
{
Console.Write("Task " + i);
for (int j = 0; j < cols; j++)
{
if (x[i, j].Value() == 1)
{
Console.WriteLine(" is done by " + j);
}
}
}
Console.WriteLine();
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Hidato puzzle in Google CP Solver.
*
* http://www.hidato.com/
* """
* Puzzles start semi-filled with numbered tiles.
* The first and last numbers are circled.
* Connect the numbers together to win. Consecutive
* number must touch horizontally, vertically, or
* diagonally.
* """
*
* This is a port of the Python model hidato_table.py
* made by Laurent Perron (using AllowedAssignments),
* based on my (much slower) model hidato.py.
*
*/
private static void Solve(int model = 1)
{
Solver solver = new Solver("HidatoTable");
//
// models, a 0 indicates an open cell which number is not yet known.
//
int[,] puzzle = null;
if (model == 1)
{
// Simple problem
// Solution 1:
// 6 7 9
// 5 2 8
// 1 4 3
int[,] puzzle1 = { { 6, 0, 9 }, { 0, 2, 8 }, { 1, 0, 0 } };
puzzle = puzzle1;
}
else if (model == 2)
{
int[,] puzzle2 = { { 0, 44, 41, 0, 0, 0, 0 }, { 0, 43, 0, 28, 29, 0, 0 },
{ 0, 1, 0, 0, 0, 33, 0 }, { 0, 2, 25, 4, 34, 0, 36 },
{ 49, 16, 0, 23, 0, 0, 0 }, { 0, 19, 0, 0, 12, 7, 0 },
{ 0, 0, 0, 14, 0, 0, 0 } };
puzzle = puzzle2;
}
else if (model == 3)
{
// Problems from the book:
// Gyora Bededek: "Hidato: 2000 Pure Logic Puzzles"
// Problem 1 (Practice)
int[,] puzzle3 = { { 0, 0, 20, 0, 0 },
{ 0, 0, 0, 16, 18 },
{ 22, 0, 15, 0, 0 },
{ 23, 0, 1, 14, 11 },
{ 0, 25, 0, 0, 12 } };
puzzle = puzzle3;
}
else if (model == 4)
{
// problem 2 (Practice)
int[,] puzzle4 = { { 0, 0, 0, 0, 14 },
{ 0, 18, 12, 0, 0 },
{ 0, 0, 17, 4, 5 },
{ 0, 0, 7, 0, 0 },
{ 9, 8, 25, 1, 0 } };
puzzle = puzzle4;
}
else if (model == 5)
{
// problem 3 (Beginner)
int[,] puzzle5 = { { 0, 26, 0, 0, 0, 18 }, { 0, 0, 27, 0, 0, 19 }, { 31, 23, 0, 0, 14, 0 },
{ 0, 33, 8, 0, 15, 1 }, { 0, 0, 0, 5, 0, 0 }, { 35, 36, 0, 10, 0, 0 } };
puzzle = puzzle5;
}
else if (model == 6)
{
// Problem 15 (Intermediate)
int[,] puzzle6 = { { 64, 0, 0, 0, 0, 0, 0, 0 }, { 1, 63, 0, 59, 15, 57, 53, 0 },
{ 0, 4, 0, 14, 0, 0, 0, 0 }, { 3, 0, 11, 0, 20, 19, 0, 50 },
{ 0, 0, 0, 0, 22, 0, 48, 40 }, { 9, 0, 0, 32, 23, 0, 0, 41 },
{ 27, 0, 0, 0, 36, 0, 46, 0 }, { 28, 30, 0, 35, 0, 0, 0, 0 } };
puzzle = puzzle6;
}
int r = puzzle.GetLength(0);
int c = puzzle.GetLength(1);
Console.WriteLine();
Console.WriteLine("----- Solving problem {0} -----", model);
Console.WriteLine();
PrintMatrix(puzzle);
//
// Decision variables
//
IntVar[] positions = solver.MakeIntVarArray(r * c, 0, r * c - 1, "p");
//
// Constraints
//
solver.Add(positions.AllDifferent());
//
// Fill in the clues
//
for (int i = 0; i < r; i++)
{
for (int j = 0; j < c; j++)
{
if (puzzle[i, j] > 0)
{
solver.Add(positions[puzzle[i, j] - 1] == i * c + j);
}
}
}
// Consecutive numbers much touch each other in the grid.
// We use an allowed assignment constraint to model it.
IntTupleSet close_tuples = BuildPairs(r, c);
for (int k = 1; k < r * c - 1; k++)
{
IntVar[] tmp = new IntVar[] { positions[k], positions[k + 1] };
solver.Add(tmp.AllowedAssignments(close_tuples));
}
//
// Search
//
DecisionBuilder db =
solver.MakePhase(positions, Solver.CHOOSE_MIN_SIZE_LOWEST_MIN, Solver.ASSIGN_MIN_VALUE);
solver.NewSearch(db);
int num_solution = 0;
while (solver.NextSolution())
{
num_solution++;
PrintOneSolution(positions, r, c, num_solution);
}
Console.WriteLine("\nSolutions: " + solver.Solutions());
Console.WriteLine("WallTime: " + solver.WallTime() + "ms ");
Console.WriteLine("Failures: " + solver.Failures());
Console.WriteLine("Branches: " + solver.Branches());
solver.EndSearch();
}
/**
*
* Solves the Quasigroup Completion problem.
* See http://www.hakank.org/or-tools/quasigroup_completion.py
*
*/
private static void Solve()
{
Solver solver = new Solver("QuasigroupCompletion");
//
// data
//
Console.WriteLine("Problem:");
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
Console.Write(problem[i, j] + " ");
}
Console.WriteLine();
}
Console.WriteLine();
//
// Decision variables
//
IntVar[,] x = solver.MakeIntVarMatrix(n, n, 1, n, "x");
IntVar[] x_flat = x.Flatten();
//
// Constraints
//
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
if (problem[i, j] > X)
{
solver.Add(x[i, j] == problem[i, j]);
}
}
}
//
// rows and columns must be different
//
// rows
for (int i = 0; i < n; i++)
{
IntVar[] row = new IntVar[n];
for (int j = 0; j < n; j++)
{
row[j] = x[i, j];
}
solver.Add(row.AllDifferent());
}
// columns
for (int j = 0; j < n; j++)
{
IntVar[] col = new IntVar[n];
for (int i = 0; i < n; i++)
{
col[i] = x[i, j];
}
solver.Add(col.AllDifferent());
}
//
// Search
//
DecisionBuilder db = solver.MakePhase(x_flat,
Solver.INT_VAR_SIMPLE,
Solver.ASSIGN_MIN_VALUE);
solver.NewSearch(db);
int sol = 0;
while (solver.NextSolution())
{
sol++;
Console.WriteLine("Solution #{0} ", sol + " ");
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
Console.Write("{0} ", x[i, j].Value());
}
Console.WriteLine();
}
Console.WriteLine();
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
public void NewSearch(DecisionBuilder db) {
pinned_decision_builder_ = db;
pinned_search_monitors_.Clear();
NewSearchAux(db);
}
/**
*
* Lectures problem in Google CP Solver.
*
* Biggs: Discrete Mathematics (2nd ed), page 187.
* """
* Suppose we wish to schedule six one-hour lectures, v1, v2, v3, v4, v5, v6.
* Among the the potential audience there are people who wish to hear both
*
* - v1 and v2
* - v1 and v4
* - v3 and v5
* - v2 and v6
* - v4 and v5
* - v5 and v6
* - v1 and v6
*
* How many hours are necessary in order that the lectures can be given
* without clashes?
* """
*
* Note: This can be seen as a coloring problem.
*
* Also see http://www.hakank.org/or-tools/lectures.py
*
*/
private static void Solve()
{
Solver solver = new Solver("Lectures");
//
// The schedule requirements:
// lecture a cannot be held at the same time as b
// Note: 1-based (compensated in the constraints).
int[,] g = { { 1, 2 }, { 1, 4 }, { 3, 5 }, { 2, 6 }, { 4, 5 }, { 5, 6 }, { 1, 6 } };
// number of nodes
int n = 6;
// number of edges
int edges = g.GetLength(0);
//
// Decision variables
//
//
// declare variables
//
IntVar[] v = solver.MakeIntVarArray(n, 0, n - 1, "v");
// Maximum color (hour) to minimize.
// Note: since C# is 0-based, the
// number of colors is max_c+1.
IntVar max_c = v.Max().VarWithName("max_c");
//
// Constraints
//
// Ensure that there are no clashes
// also, adjust to 0-base.
for (int i = 0; i < edges; i++)
{
solver.Add(v[g[i, 0] - 1] != v[g[i, 1] - 1]);
}
// Symmetry breaking:
// - v0 has the color 0,
// - v1 has either color 0 or 1
solver.Add(v[0] == 0);
solver.Add(v[1] <= 1);
//
// Objective
//
OptimizeVar obj = max_c.Minimize(1);
//
// Search
//
DecisionBuilder db =
solver.MakePhase(v, Solver.CHOOSE_MIN_SIZE_LOWEST_MIN, Solver.ASSIGN_MIN_VALUE);
solver.NewSearch(db, obj);
while (solver.NextSolution())
{
Console.WriteLine("\nmax hours: {0}", max_c.Value() + 1);
Console.WriteLine(
"v: " +
String.Join(" ", (from i in Enumerable.Range(0, n) select v[i].Value()).ToArray()));
for (int i = 0; i < n; i++)
{
Console.WriteLine("Lecture {0} at {1}h", i, v[i].Value());
}
Console.WriteLine("\n");
}
Console.WriteLine("\nSolutions: " + solver.Solutions());
Console.WriteLine("WallTime: " + solver.WallTime() + "ms ");
Console.WriteLine("Failures: " + solver.Failures());
Console.WriteLine("Branches: " + solver.Branches());
solver.EndSearch();
}
public void NewSearch(DecisionBuilder db, SearchMonitor[] monitors) {
pinned_decision_builder_ = db;
pinned_search_monitors_.Clear();
pinned_search_monitors_.AddRange(monitors);
NewSearchAux(db, monitors);
}
/**
*
* Solves a set covering problem.
* See See http://www.hakank.org/or-tools/set_covering.py
*
*/
private static void Solve()
{
Solver solver = new Solver("SetCovering");
//
// data
//
// Placing of firestations, from Winston 'Operations Research',
// page 486.
int min_distance = 15;
int num_cities = 6;
int[,] distance = { { 0, 10, 20, 30, 30, 20 }, { 10, 0, 25, 35, 20, 10 }, { 20, 25, 0, 15, 30, 20 },
{ 30, 35, 15, 0, 15, 25 }, { 30, 20, 30, 15, 0, 14 }, { 20, 10, 20, 25, 14, 0 } };
//
// Decision variables
//
IntVar[] x = solver.MakeIntVarArray(num_cities, 0, 1, "x");
IntVar z = x.Sum().Var();
//
// Constraints
//
// ensure that all cities are covered
for (int i = 0; i < num_cities; i++)
{
IntVar[] b = (from j in Enumerable.Range(0, num_cities)
where distance[i, j] <= min_distance select x[j])
.ToArray();
solver.Add(b.Sum() >= 1);
}
//
// objective
//
OptimizeVar objective = z.Minimize(1);
//
// Search
//
DecisionBuilder db = solver.MakePhase(x, Solver.INT_VAR_DEFAULT, Solver.INT_VALUE_DEFAULT);
solver.NewSearch(db, objective);
while (solver.NextSolution())
{
Console.WriteLine("z: {0}", z.Value());
Console.Write("x: ");
for (int i = 0; i < num_cities; i++)
{
Console.Write(x[i].Value() + " ");
}
Console.WriteLine();
}
Console.WriteLine("\nSolutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0}ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
