static void Main(string[] args)
{
var parameters = new Parameters(new Randomizer(), args);
if (parameters.ShowHelp)
{
var helpPrinter = new HelpPrinter();
helpPrinter.Print();
}
else
{
var verbosePrinter = new VerbosePrinter(parameters.IsVerboseEnabled);
var calculator = new Calculator(verbosePrinter, parameters.Target, parameters.Numbers);
if (parameters.IsUsingRandomTarget)
{
Console.WriteLine("No target number specified. Will use random number.");
Console.WriteLine($"The random target is {calculator.Target}");
Console.WriteLine();
}
if (parameters.IsUsingRandomNumbers)
{
Console.WriteLine("No numbers specified. Will use random numbers between 1 and 20.");
var numbers = string.Join(", ", calculator.InputNumbers.Select(o => o.Integer.ToString()));
Console.WriteLine($"The numbers are {numbers}");
Console.WriteLine();
}
var result = calculator.FindSolutions();
var printer = new SolutionPrinter(parameters.ShowAllResults, result);
printer.Print();
}
}
// [END solution_printer]
static void Main()
{
// Constraint programming engine
// [START model]
CpModel model = new CpModel();
// [START model]
// [START variables]
int BoardSize = 8;
IntVar[] queens = new IntVar[BoardSize];
for (int i = 0; i < BoardSize; ++i)
{
queens[i] = model.NewIntVar(0, BoardSize - 1, $"x{i}");
}
// [END variables]
// Define constraints.
// [START constraints]
// All rows must be different.
model.AddAllDifferent(queens);
// All columns must be different because the indices of queens are all different.
// No two queens can be on the same diagonal.
LinearExpr[] diag1 = new LinearExpr[BoardSize];
LinearExpr[] diag2 = new LinearExpr[BoardSize];
for (int i = 0; i < BoardSize; ++i)
{
diag1[i] = LinearExpr.Affine(queens[i], /*coeff=*/ 1, /*offset=*/ i);
diag2[i] = LinearExpr.Affine(queens[i], /*coeff=*/ 1, /*offset=*/ -i);
}
model.AddAllDifferent(diag1);
model.AddAllDifferent(diag2);
// [END constraints]
// [START solve]
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
SolutionPrinter cb = new SolutionPrinter(queens);
// Search for all solutions.
solver.StringParameters = "enumerate_all_solutions:true";
// And solve.
solver.Solve(model, cb);
// [END solve]
// [START statistics]
Console.WriteLine("Statistics");
Console.WriteLine($" conflicts : {solver.NumConflicts()}");
Console.WriteLine($" branches : {solver.NumBranches()}");
Console.WriteLine($" wall time : {solver.WallTime()} s");
Console.WriteLine($" number of solutions found: {cb.SolutionCount()}");
// [END statistics]
}
static void Main(string[] args)
{
int numberGroups = 10;
int numberItems = 100;
int numberColors = 3;
int minItemsOfSameColorPerGroup = 4;
var allGroups = Enumerable.Range(0, numberGroups).ToArray();
var allItems = Enumerable.Range(0, numberItems).ToArray();
var allColors = Enumerable.Range(0, numberColors).ToArray();
var values = allItems.Select(i => 1 + i + (i * i / 200)).ToArray();
var colors = allItems.Select(i => i % numberColors).ToArray();
var sumOfValues = values.Sum();
var averageSumPerGroup = sumOfValues / numberGroups;
var numItemsPerGroup = numberItems / numberGroups;
var itemsPerColor = new Dictionary <int, List <int> >();
foreach (var color in allColors)
{
itemsPerColor[color] = new List <int>();
foreach (var item in allItems)
{
if (colors[item] == color)
{
itemsPerColor[color].Add(item);
}
}
}
Console.WriteLine($"Model has {numberItems}, {numberGroups} groups and {numberColors} colors");
Console.WriteLine($" Average sum per group = {averageSumPerGroup}");
var model = new CpModel();
var itemInGroup = new IntVar[numberItems, numberGroups];
foreach (var item in allItems)
{
foreach (var @group in allGroups)
{
itemInGroup[item, @group] = model.NewBoolVar($"item {item} in group {@group}");
}
}
// Each group must have the same size.
foreach (var @group in allGroups)
{
var itemsInGroup = allItems.Select(x => itemInGroup[x, @group]).ToArray();
model.AddLinearConstraint(LinearExpr.Sum(itemsInGroup), numItemsPerGroup, numItemsPerGroup);
}
//# One item must belong to exactly one group.
foreach (var item in allItems)
{
var groupsForItem = allGroups.Select(x => itemInGroup[item, x]).ToArray();
model.Add(LinearExpr.Sum(groupsForItem) == 1);
}
// The deviation of the sum of each items in a group against the average.
var e = model.NewIntVar(0, 550, "epsilon");
// Constrain the sum of values in one group around the average sum per
// group.
foreach (var @group in allGroups)
{
var itemValues = allItems.Select(x => itemInGroup[x, @group]).ToArray();
var sum = LinearExpr.ScalProd(itemValues, values);
model.Add(sum <= averageSumPerGroup + e);
model.Add(sum >= averageSumPerGroup - e);
}
// colorInGroup variables.
var colorInGroup = new IntVar[numberColors, numberGroups];
foreach (var @group in allGroups)
{
foreach (var color in allColors)
{
colorInGroup[color, @group] = model.NewBoolVar($"color {color} is in group {@group}");
}
}
// Item is in a group implies its color is in that group.
foreach (var item in allItems)
{
foreach (var @group in allGroups)
{
model.AddImplication(itemInGroup[item, @group], colorInGroup[colors[item], @group]);
}
}
// If a color is in a group, it must contains at least
// min_items_of_same_color_per_group items from that color.
foreach (var color in allColors)
{
foreach (var @group in allGroups)
{
var literal = colorInGroup[color, @group];
var items = itemsPerColor[color].Select(x => itemInGroup[x, @group]).ToArray();
model.Add(LinearExpr.Sum(items) >= minItemsOfSameColorPerGroup).OnlyEnforceIf(literal);
}
}
// Compute the maximum number of colors in a group.
int maxColor = numItemsPerGroup / minItemsOfSameColorPerGroup;
// Redundant contraint: The problem does not solve in reasonable time
// without it.
if (maxColor < numberColors)
{
foreach (var @group in allGroups)
{
var all = allColors.Select(x => colorInGroup[x, @group]).ToArray();
model.Add(LinearExpr.Sum(all) <= maxColor);
}
}
// Minimize epsilon
model.Minimize(e);
var solver = new CpSolver();
var solutionPrinter = new SolutionPrinter(values, colors, allGroups, allItems, itemInGroup);
var status = solver.SolveWithSolutionCallback(model, solutionPrinter);
}
// [END solution_printer]
public static void Main(String[] args)
{
// [START data]
const int numNurses = 4;
const int numDays = 3;
const int numShifts = 3;
int[] allNurses = Enumerable.Range(0, numNurses).ToArray();
int[] allDays = Enumerable.Range(0, numDays).ToArray();
int[] allShifts = Enumerable.Range(0, numShifts).ToArray();
// [END data]
// Creates the model.
// [START model]
CpModel model = new CpModel();
// [END model]
// Creates shift variables.
// shifts[(n, d, s)]: nurse 'n' works shift 's' on day 'd'.
// [START variables]
Dictionary <Tuple <int, int, int>, IntVar> shifts = new Dictionary <Tuple <int, int, int>, IntVar>();
foreach (int n in allNurses)
{
foreach (int d in allDays)
{
foreach (int s in allShifts)
{
shifts.Add(Tuple.Create(n, d, s), model.NewBoolVar($"shifts_n{n}d{d}s{s}"));
}
}
}
// [END variables]
// Each shift is assigned to exactly one nurse in the schedule period.
// [START exactly_one_nurse]
foreach (int d in allDays)
{
foreach (int s in allShifts)
{
IntVar[] x = new IntVar[numNurses];
foreach (int n in allNurses)
{
var key = Tuple.Create(n, d, s);
x[n] = shifts[key];
}
model.Add(LinearExpr.Sum(x) == 1);
}
}
// [END exactly_one_nurse]
// Each nurse works at most one shift per day.
// [START at_most_one_shift]
foreach (int n in allNurses)
{
foreach (int d in allDays)
{
IntVar[] x = new IntVar[numShifts];
foreach (int s in allShifts)
{
var key = Tuple.Create(n, d, s);
x[s] = shifts[key];
}
model.Add(LinearExpr.Sum(x) <= 1);
}
}
// [END at_most_one_shift]
// [START assign_nurses_evenly]
// Try to distribute the shifts evenly, so that each nurse works
// minShiftsPerNurse shifts. If this is not possible, because the total
// number of shifts is not divisible by the number of nurses, some nurses will
// be assigned one more shift.
int minShiftsPerNurse = (numShifts * numDays) / numNurses;
int maxShiftsPerNurse;
if ((numShifts * numDays) % numNurses == 0)
{
maxShiftsPerNurse = minShiftsPerNurse;
}
else
{
maxShiftsPerNurse = minShiftsPerNurse + 1;
}
foreach (int n in allNurses)
{
IntVar[] numShiftsWorked = new IntVar[numDays * numShifts];
foreach (int d in allDays)
{
foreach (int s in allShifts)
{
var key = Tuple.Create(n, d, s);
numShiftsWorked[d * numShifts + s] = shifts[key];
}
}
model.AddLinearConstraint(LinearExpr.Sum(numShiftsWorked), minShiftsPerNurse, maxShiftsPerNurse);
}
// [END assign_nurses_evenly]
// [START parameters]
CpSolver solver = new CpSolver();
solver.StringParameters += "linearization_level:0 ";
// Tell the solver to enumerate all solutions.
solver.StringParameters += "enumerate_all_solutions:true ";
// [END parameters]
// Display the first five solutions.
// [START solution_printer_instantiate]
const int solutionLimit = 5;
SolutionPrinter cb = new SolutionPrinter(allNurses, allDays, allShifts, shifts, solutionLimit);
// [END solution_printer_instantiate]
// Solve
// [START solve]
CpSolverStatus status = solver.Solve(model, cb);
Console.WriteLine($"Solve status: {status}");
// [END solve]
// [START statistics]
Console.WriteLine("Statistics");
Console.WriteLine($" conflicts: {solver.NumConflicts()}");
Console.WriteLine($" branches : {solver.NumBranches()}");
Console.WriteLine($" wall time: {solver.WallTime()}s");
// [END statistics]
}
