static void Main()
{
// [START solver]
LinearSumAssignment assignment = new LinearSumAssignment();
// [END solver]
// [START data]
int[,] costs =
{
{ 90, 76, 75, 70 },
{ 35, 85, 55, 65 },
{ 125, 95, 90, 105 },
{ 45, 110, 95, 115 },
};
int numWorkers = 4;
int[] allWorkers = Enumerable.Range(0, numWorkers).ToArray();
int numTasks = 4;
int[] allTasks = Enumerable.Range(0, numTasks).ToArray();
// [END data]
// [START constraints]
// Add each arc.
foreach (int w in allWorkers)
{
foreach (int t in allTasks)
{
if (costs[w, t] != 0)
{
assignment.AddArcWithCost(w, t, costs[w, t]);
}
}
}
// [END constraints]
// [START solve]
LinearSumAssignment.Status status = assignment.Solve();
// [END solve]
// [START print_solution]
if (status == LinearSumAssignment.Status.OPTIMAL)
{
Console.WriteLine($"Total cost: {assignment.OptimalCost()}.");
foreach (int worker in allWorkers)
{
Console.WriteLine($"Worker {worker} assigned to task {assignment.RightMate(worker)}. " +
$"Cost: {assignment.AssignmentCost(worker)}.");
}
}
else
{
Console.WriteLine("Solving the linear assignment problem failed.");
Console.WriteLine($"Solver status: {status}.");
}
// [END print_solution]
}
public override Decision Next(Solver solver)
{
int large = 100000;
int number_of_variables = vars_.Length;
long last_slot = last_slot_var_.Max();
// Lets build a bipartite graph with equal number of nodes left and right.
// Variables will be on the left, slots on the right.
// We will add dummy variables when needed.
// Arcs will have a cost x is slot x is possible for a variable, a large
// number otherwise. For dummy variables, the cost will be 0 always.
LinearSumAssignment matching = new LinearSumAssignment();
for (int speaker = 0; speaker < number_of_variables; ++speaker)
{
IntVar var = vars_[speaker];
for (int value = first_slot_; value <= last_slot; ++value)
{
if (var.Contains(value))
{
matching.AddArcWithCost(speaker, value - first_slot_, value);
}
else
{
matching.AddArcWithCost(speaker, value - first_slot_, large);
}
}
}
// The Matching algorithms expect the same number of left and right nodes.
// So we fill the rest with dense zero-cost arcs.
for (int dummy = number_of_variables;
dummy <= last_slot - first_slot_; ++dummy)
{
for (int value = first_slot_; value <= last_slot; ++value)
{
matching.AddArcWithCost(dummy, value - first_slot_, 0);
}
}
if (matching.Solve() == LinearSumAssignment.OPTIMAL &&
matching.OptimalCost() < large) // No violated arcs.
{
for (int speaker = 0; speaker < number_of_variables; ++speaker)
{
vars_[speaker].SetValue(matching.RightMate(speaker) + first_slot_);
}
}
else
{
solver.Fail();
}
return(null);
}
public override Decision Next(Solver solver)
{
int large = 100000;
int number_of_variables = vars_.Length;
long last_slot = last_slot_var_.Max();
// Lets build a bipartite graph with equal number of nodes left and right.
// Variables will be on the left, slots on the right.
// We will add dummy variables when needed.
// Arcs will have a cost x is slot x is possible for a variable, a large
// number otherwise. For dummy variables, the cost will be 0 always.
LinearSumAssignment matching = new LinearSumAssignment();
for (int speaker = 0; speaker < number_of_variables; ++speaker)
{
IntVar var = vars_[speaker];
for (int value = first_slot_; value <= last_slot; ++value)
{
if (var.Contains(value))
{
matching.AddArcWithCost(speaker, value - first_slot_, value);
}
else
{
matching.AddArcWithCost(speaker, value - first_slot_, large);
}
}
}
// The Matching algorithms expect the same number of left and right nodes.
// So we fill the rest with dense zero-cost arcs.
for (int dummy = number_of_variables;
dummy <= last_slot - first_slot_; ++dummy) {
for (int value = first_slot_; value <= last_slot; ++value)
{
matching.AddArcWithCost(dummy, value - first_slot_, 0);
}
}
if (matching.Solve() == LinearSumAssignment.OPTIMAL &&
matching.OptimalCost() < large) // No violated arcs.
{
for (int speaker = 0; speaker < number_of_variables; ++speaker)
{
vars_[speaker].SetValue(matching.RightMate(speaker) + first_slot_);
}
} else {
solver.Fail();
}
return null;
}
private static void Run(int[][] costs)
{
var botsCount = costs.Length;
var tasksCount = costs[0].Length;
Console.WriteLine($"*** Start with Bots = {botsCount} , tasks = {tasksCount} ***");
// Solver usage
var assignment = new LinearSumAssignment();
for (var bot = 0; bot < botsCount; bot++)
{
for (var task = 0; task < tasksCount; task++)
{
assignment.AddArcWithCost(bot, task, costs[bot][task]);
}
}
var solveStatus = assignment.Solve();
// Check the result
if (solveStatus == LinearSumAssignment.Status.OPTIMAL)
{
Console.WriteLine($" Optimal Cost = {assignment.OptimalCost()}");
for (int i = 0; i < assignment.NumNodes(); i++)
{
Console.WriteLine($"Task {assignment.RightMate(i)} assigned to Bot {i}, distance {assignment.AssignmentCost(i)}");
}
}
else if (solveStatus == LinearSumAssignment.Status.INFEASIBLE)
{
Console.WriteLine("No assignment is possible.");
}
else if (solveStatus == LinearSumAssignment.Status.POSSIBLE_OVERFLOW)
{
Console.WriteLine("Some input costs are too large and may cause an integer overflow.");
}
}
