void Solve(int limit)
{
// Declares the optimization model.
LSModel model = localsolver.GetModel();
trucksUsed = new LSExpression[nbTrucks];
customersSequences = new LSExpression[nbTrucks];
routeDistances = new LSExpression[nbTrucks];
// Sequence of customers visited by each truck.
for (int k = 0; k < nbTrucks; k++)
{
customersSequences[k] = model.List(nbCustomers);
}
// All customers must be visited by the trucks
model.Constraint(model.Partition(customersSequences));
// Create demands and distances as arrays to be able to access it with an "at" operator
LSExpression demandsArray = model.Array(demands);
LSExpression distanceWarehouseArray = model.Array(distanceWarehouses);
LSExpression distanceArray = model.Array(distanceMatrix);
for (int k = 0; k < nbTrucks; k++)
{
LSExpression sequence = customersSequences[k];
LSExpression c = model.Count(sequence);
// A truck is used if it visits at least one customer
trucksUsed[k] = c > 0;
// The quantity needed in each route must not exceed the truck capacity
LSExpression demandSelector = model.Function(i => demandsArray[sequence[i]]);
LSExpression routeQuantity = model.Sum(model.Range(0, c), demandSelector);
model.Constraint(routeQuantity <= truckCapacity);
// Distance travelled by truck k
LSExpression distSelector = model.Function(i => distanceArray[sequence[i - 1], sequence[i]]);
routeDistances[k] = model.Sum(model.Range(1, c), distSelector)
+ model.If(c > 0, distanceWarehouseArray[sequence[0]] + distanceWarehouseArray[sequence[c - 1]], 0);
}
nbTrucksUsed = model.Sum(trucksUsed);
totalDistance = model.Sum(routeDistances);
// Objective: minimize the number of trucks used, then minimize the distance traveled
model.Minimize(nbTrucksUsed);
model.Minimize(totalDistance);
model.Close();
// Parameterizes the solver.
LSPhase phase = localsolver.CreatePhase();
phase.SetTimeLimit(limit);
localsolver.Solve();
}
void Solve(int limit)
{
// Declares the optimization model.
localsolver = new LocalSolver();
LSModel model = localsolver.GetModel();
// Decision variables x[i]
x = new LSExpression[nbItems];
for (int i = 0; i < nbItems; i++)
{
x[i] = model.Bool();
}
// weight constraint
LSExpression knapsackWeight = model.Sum();
for (int i = 0; i < nbItems; i++)
{
knapsackWeight.AddOperand(x[i] * weights[i]);
}
model.Constraint(knapsackWeight <= knapsackBound);
// maximize value
knapsackValue = model.Sum();
for (int i = 0; i < nbItems; i++)
{
knapsackValue.AddOperand(x[i] * values[i]);
}
model.Maximize(knapsackValue);
model.Close();
// Parameterizes the solver.
localsolver.GetParam().SetTimeLimit(limit);
localsolver.Solve();
solutions = new List <int>();
for (int i = 0; i < nbItems; ++i)
{
if (x[i].GetValue() == 1)
{
solutions.Add(i);
}
}
}
