CPlexSolver::CPlexSolver(const Matrix& A, unsigned int lambda) :
env(), model(env), vars(env), constraints(env),
solutionVectors() {
// Add variables
for (int i = 0; i < A.shape()[1]; i++) {
vars.add(IloBoolVar(env)); // For simple t-designs
}
// Add constraints
for (int i = 0; i < A.shape()[0]; i++) {
constraints.add(IloRange(env, lambda, lambda)); // RHS = lambda in every equation
}
// Set up model to have as few block orbits as possible
IloObjective objective = IloMinimize(env);
for (int i = 0; i < A.shape()[1]; i++) {
objective.setLinearCoef(vars[i], 1);
}
// Set up constraints according to input matrix
for (int i = 0; i < A.shape()[0]; i++) {
for (int j = 0; j < A.shape()[1]; j++) {
constraints[i].setLinearCoef(vars[j], A[i][j]);
}
}
// TODO - names for constraints/vars may be necessary
// Might have to name these after the row/col labels for K-M Matrix
// Finish setting up the model
model.add(objective);
model.add(constraints);
}
/* initialisiere N^2 Variablen und erstelle eine zu minimierende
* Strecken-Zielfunktion über die Distanzmatrix c
*
* eigentlich sind nur (N*N-N)/2 Variablen nötig, aber dafür müsste
* ich mir etwas schlaues zur Adressierung ausdenken (weil das untere linke
* Dreieck einer Matrix adressiert werden muss, ist das nicht trivial)
* Der Presolver scheint die überflüssigen Variablen allerdings
* direkt zu verwerfen, weshalb das nicht dringend ist.
* */
IloNumVarArray CplexTSPSolver::init_symmetric_var(IloModel model)
{
IloEnv env = model.getEnv();
// Edge Variables
IloNumVarArray x(env);
for(int i=0; i<N; i++)
for(int j=0; j<N; j++)
if(j<i)
x.add(IloNumVar(env, 0, 1, mip ? ILOINT : ILOFLOAT));
else
x.add(IloNumVar(env, 0, 0, ILOFLOAT)); // fülle oben rechts mit dummies
model.add(x);
// Cost Function
IloExpr expr(env);
// die folgenden Schleifen adressieren ein unteres linkes
// Dreieck in einer quadratischen Matrix
for(int i=0; i<N; i++)
for (int j=0; j<i; j++)
expr += c[i*N + j] * x[i*N + j];
model.add(IloMinimize(env, expr));
expr.end();
return x;
}
ILOSTLBEGIN
int
main()
{
IloEnv env;
try {
IloModel model(env, "chvatal");
IloNumVarArray x(env, 12, 0.0, 50.0);
model.add(IloMinimize(env, x[0] + x[1] + x[2] + x[3] + x[4] +
x[5] + x[6] + 2*x[10] + 2*x[11] ));
model.add( -x[7]-x[8]-x[9]
== -1);
model.add( x[0] +x[5] +x[7]
== 4);
model.add( x[1] +x[3] +x[6] +x[8]
== 1);
model.add( x[2] +x[4] +x[9]
== 1);
model.add( -x[5]-x[6] -x[10]+x[11]
== -2);
model.add( -x[3]-x[4] +x[10]
== -2);
model.add(-x[0]-x[1]-x[2] -x[11]
== -1);
IloCplex cplex(model);
cplex.setParam(IloCplex::Param::Simplex::Display, 2);
cplex.setParam(IloCplex::Param::RootAlgorithm, IloCplex::Network);
cplex.solve();
cplex.out() << "After network optimization, objective is "
<< cplex.getObjValue() << endl;
model.add(2*x[10] + 5*x[11] == 2);
model.add( x[0] + x[2] + x[5] == 3);
cplex.setParam(IloCplex::Param::RootAlgorithm, IloCplex::Dual);
cplex.solve();
IloNumArray vals(env);
cplex.getValues(vals, x);
cplex.out() << "Solution status " << cplex.getStatus() << endl;
cplex.out() << "Objective value " << cplex.getObjValue() << endl;
cplex.out() << "Solution is: " << vals << endl;
cplex.exportModel("lpex3.lp");
}
catch (IloException& e) {
cerr << "Concert exception caught: " << e << endl;
}
catch (...) {
cerr << "Unknown exception caught" << endl;
}
env.end();
return 0;
} // END main
void update()
{
switch (dir_)
{
case IP::MAX: model_.add(IloMaximize(env_, obj_)); break;
case IP::MIN: model_.add(IloMinimize(env_, obj_)); break;
}
}
/**
* Objective function:
* $\sum_{i, j} c_{ij} x_{ij}$
*/
static void addObjectiveFunction(IloEnv env, IloModel model, IloBoolVarArray xs, vector<Instance::Edge> edges, u_int n_edges)
{
IloExpr e_objective(env);
for (u_int m = 0; m < n_edges; m++) {
e_objective += xs[m] * edges[m].weight;
}
model.add(IloMinimize(env, e_objective));
e_objective.end();
}
// This routine creates the master ILP (arc variables x and degree constraints).
//
// Modeling variables:
// forall (i,j) in A:
// x(i,j) = 1, if arc (i,j) is selected
// = 0, otherwise
//
// Objective:
// minimize sum((i,j) in A) c(i,j) * x(i,j)
//
// Degree constraints:
// forall i in V: sum((i,j) in delta+(i)) x(i,j) = 1
// forall i in V: sum((j,i) in delta-(i)) x(j,i) = 1
//
// Binary constraints on arc variables:
// forall (i,j) in A: x(i,j) in {0, 1}
//
void
createMasterILP(IloModel mod, Arcs x, IloNumArray2 arcCost)
{
IloInt i, j;
IloEnv env = mod.getEnv();
IloInt numNodes = x.getSize();
// Create variables x(i,j) for (i,j) in A
// For simplicity, also dummy variables x(i,i) are created.
// Those variables are fixed to 0 and do not partecipate to
// the constraints.
char varName[100];
for (i = 0; i < numNodes; ++i) {
x[i] = IloIntVarArray(env, numNodes, 0, 1);
x[i][i].setBounds(0, 0);
for (j = 0; j < numNodes; ++j) {
sprintf(varName, "x.%d.%d", (int) i, (int) j);
x[i][j].setName(varName);
}
mod.add(x[i]);
}
// Create objective function: minimize sum((i,j) in A ) c(i,j) * x(i,j)
IloExpr obj(env);
for (i = 0; i < numNodes; ++i) {
arcCost[i][i] = 0;
obj += IloScalProd(x[i], arcCost[i]);
}
mod.add(IloMinimize(env, obj));
obj.end();
// Add the out degree constraints.
// forall i in V: sum((i,j) in delta+(i)) x(i,j) = 1
for (i = 0; i < numNodes; ++i) {
IloExpr expr(env);
for (j = 0; j < i; ++j) expr += x[i][j];
for (j = i+1; j < numNodes; ++j) expr += x[i][j];
mod.add(expr == 1);
expr.end();
}
// Add the in degree constraints.
// forall i in V: sum((j,i) in delta-(i)) x(j,i) = 1
for (i = 0; i < numNodes; i++) {
IloExpr expr(env);
for (j = 0; j < i; j++) expr += x[j][i];
for (j = i+1; j < numNodes; j++) expr += x[j][i];
mod.add(expr == 1);
expr.end();
}
}// END createMasterILP
int createObj(const Problem<double>& P,IloEnv& env, IloModel& model,
IloNumVarMatrix& b){
IloExpr expr(env);
for (int i=0;i<P.nbTask;++i) {
for (int e=0;e<2*P.nbTask-1;++e)
expr+=b[i][e];
}
model.add(IloMinimize(env,expr));
expr.end();
return 0;
}
void kMST_ILP::addObjectiveFunction()
{
// multiply variable by cost
IloIntArray edgeCost(env, instance.n_edges * 2);
for (unsigned int i=0; i<edges.getSize(); i++) {
edgeCost[i] = instance.edges[i % instance.n_edges].weight;
}
model.add(IloMinimize(env, IloScalProd(edges, edgeCost) ));
}
int main(int, const char * []) {
IloEnv env;
try {
IloInt i,j;
IloModel model(env);
IloNumExpr cost(env);
IloIntervalVarArray allTasks(env);
IloIntervalVarArray joeTasks(env);
IloIntervalVarArray jimTasks(env);
IloIntArray joeLocations(env);
IloIntArray jimLocations(env);
MakeHouse(model, cost, allTasks, joeTasks, jimTasks, joeLocations,
jimLocations, 0, 0, 120, 100.0);
MakeHouse(model, cost, allTasks, joeTasks, jimTasks, joeLocations,
jimLocations, 1, 0, 212, 100.0);
MakeHouse(model, cost, allTasks, joeTasks, jimTasks, joeLocations,
jimLocations, 2, 151, 304, 100.0);
MakeHouse(model, cost, allTasks, joeTasks, jimTasks, joeLocations,
jimLocations, 3, 59, 181, 200.0);
MakeHouse(model, cost, allTasks, joeTasks, jimTasks, joeLocations,
jimLocations, 4, 243, 425, 100.0);
IloTransitionDistance tt(env, 5);
for (i=0; i<5; ++i)
for (j=0; j<5; ++j)
tt.setValue(i, j, IloAbs(i-j));
IloIntervalSequenceVar joe(env, joeTasks, joeLocations, "Joe");
IloIntervalSequenceVar jim(env, jimTasks, jimLocations, "Jim");
model.add(IloNoOverlap(env, joe, tt));
model.add(IloNoOverlap(env, jim, tt));
model.add(IloMinimize(env, cost));
/* EXTRACTING THE MODEL AND SOLVING. */
IloCP cp(model);
if (cp.solve()) {
cp.out() << "Solution with objective " << cp.getObjValue() << ":" << std::endl;
for (i=0; i<allTasks.getSize(); ++i) {
cp.out() << cp.domain(allTasks[i]) << std::endl;
}
} else {
cp.out() << "No solution found. " << std::endl;
}
} catch (IloException& ex) {
env.out() << "Error: " << ex << std::endl;
}
env.end();
return 0;
}
void CPLEXSolver::add_in_constraint(LinearConstraint *con, double coef){
DBG("Creating a Gurobi representation of a constriant %s\n", "");
IloNumArray weights(*env, (IloInt)con->_coefficients.size());
IloNumVarArray vars(*env, (IloInt)con->_variables.size());
for(unsigned int i = 0; i < con->_variables.size(); ++i){
DBG("\tAdding variable to CPLEX\n%s", "");
IloNumVar var_ptr;
if(con->_variables[i]->_var == NULL){
IloNumVar::Type type;
if(con->_variables[i]->_continuous) type = IloNumVar::Float;
// else if(con->_lower == 0 && con->_upper == 1) type = IloNumVar::Bool;
else type = IloNumVar::Int;
var_ptr = IloNumVar(getEnv(),
con->_variables[i]->_lower, // LB
con->_variables[i]->_upper, // UB
type);
int *var_id = new int;
*var_id = variables->getSize();
variables->add(var_ptr);
con->_variables[i]->_var = (void*) var_id;
DBG("Created new variable with id %d. type:%c lb:%f ub:%f coef:%f\n", *var_id, type, con->_variables[i]->_lower, con->_variables[i]->_upper, coef);
} else {
var_ptr = (*variables)[*(int*)(con->_variables[i]->_var)];
}
vars[i] = (*variables)[*(int*)(con->_variables[i]->_var)];
weights[i] = con->_coefficients[i];
}
IloNumExprArg lin_expr = IloScalProd(weights, vars);
if(coef < -0.1){
model->add(IloMinimize(*env, lin_expr));
} else if(coef > 0.1){
model->add(IloMaximize(*env, lin_expr));
} else {
if(con->_lhs > -INFINITY && con->_rhs < INFINITY){
if(con->_lhs == con->_rhs) {
model->add(lin_expr == con->_lhs);
} else {
model->add(IloRange(*env, con->_lhs, lin_expr, con->_rhs));
}
} else if(con->_lhs > -INFINITY) model->add(lin_expr >= con->_lhs);
else if(con->_rhs < INFINITY) model->add(lin_expr <= con->_rhs);
}
}
static void
populatebyrow (IloModel model, IloNumVarArray x, IloRangeArray c)
{
IloEnv env = model.getEnv();
x.add(IloNumVar(env, -1.0, 1.0));
x.add(IloNumVar(env, 0.0, 1.0));
model.add(IloMinimize(env, 0.5 * (-3*x[0]*x[0] - 3*x[1]*x[1] +
- 1*x[0]*x[1] ) ));
c.add( - x[0] + x[1] >= 0);
c.add( x[0] + x[1] >= 0);
model.add(c);
} // END populatebyrow
int main(int argc, const char *argv[]){
IloEnv env;
try {
IloModel model(env);
IloInt nbTransmitters = GetTransmitterIndex(nbCell, 0);
IloIntVarArray freq(env, nbTransmitters, 0, nbAvailFreq - 1);
freq.setNames("freq");
for (IloInt cell = 0; cell < nbCell; cell++)
for (IloInt channel1 = 0; channel1 < nbChannel[cell]; channel1++)
for (IloInt channel2= channel1+1; channel2 < nbChannel[cell]; channel2++)
model.add(IloAbs( freq[GetTransmitterIndex(cell, channel1)]
- freq[GetTransmitterIndex(cell, channel2)] )
>= 16);
for (IloInt cell1 = 0; cell1 < nbCell; cell1++)
for (IloInt cell2 = cell1+1; cell2 < nbCell; cell2++)
if (dist[cell1][cell2] > 0)
for (IloInt channel1 = 0; channel1 < nbChannel[cell1]; channel1++)
for (IloInt channel2 = 0; channel2 < nbChannel[cell2]; channel2++)
model.add(IloAbs( freq[GetTransmitterIndex(cell1, channel1)]
- freq[GetTransmitterIndex(cell2, channel2)] )
>= dist[cell1][cell2]);
// Minimizing the total number of frequencies
IloIntExpr nbFreq = IloCountDifferent(freq);
model.add(IloMinimize(env, nbFreq));
IloCP cp(model);
cp.setParameter(IloCP::CountDifferentInferenceLevel, IloCP::Extended);
cp.setParameter(IloCP::FailLimit, 40000);
cp.setParameter(IloCP::LogPeriod, 100000);
if (cp.solve()) {
for (IloInt cell = 0; cell < nbCell; cell++) {
for (IloInt channel = 0; channel < nbChannel[cell]; channel++)
cp.out() << cp.getValue(freq[GetTransmitterIndex(cell, channel)])
<< " " ;
cp.out() << std::endl;
}
cp.out() << "Total # of sites " << nbTransmitters << std::endl;
cp.out() << "Total # of frequencies " << cp.getValue(nbFreq) << std::endl;
} else
cp.out() << "No solution found." << std::endl;
cp.end();
} catch (IloException & ex) {
env.out() << "Caught: " << ex << std::endl;
}
env.end();
return 0;
}
int
main()
{
IloEnv env;
try {
IloModel model(env);
setData(env);
IloNumVarArray inside(env, nbProds);
IloNumVarArray outside(env, nbProds);
IloObjective obj = IloAdd(model, IloMinimize(env));
// Must meet demand for each product
for(IloInt p = 0; p < nbProds; p++) {
IloRange demRange = IloAdd(model,
IloRange (env, demand[p], demand[p]));
inside[p] = IloNumVar(obj(insideCost[p]) + demRange(1));
outside[p] = IloNumVar(obj(outsideCost[p]) + demRange(1));
}
// Must respect capacity constraint for each resource
for(IloInt r = 0; r < nbResources; r++)
model.add(IloScalProd(consumption[r], inside) <= capacity[r]);
IloCplex cplex(env);
cplex.extract(model);
cplex.solve();
if (cplex.getStatus() != IloAlgorithm::Optimal)
cout << "No optimal solution" << endl;
cout << "Solution status: " << cplex.getStatus() << endl;
displayResults(cplex, inside, outside);
cout << "----------------------------------------" << endl;
}
catch (IloException& ex) {
cerr << "Error: " << ex << endl;
}
catch (...) {
cerr << "Error" << endl;
}
env.end();
return 0;
}
void CPLEX_LP_IMSTSolver_v2::generateGoal() {
IloExpr goalFormula(env);
IloInt i { };
IloInt varArraySize { };
EdgeIF* edge { };
INFO_NOARG(logger, LogBundleKey::CPLPIMST2_BUILD_MODEL_GOAL_CONSTRAINT);
graph->beginEdge();
while (graph->hasNextEdge(Connectivity::CONNECTED, Visibility::VISIBLE)) {
edge = graph->nextEdge();
INFO(logger, LogBundleKey::CPLPIMST2_BUILD_MODEL_GOAL_ADD,
edge->getSourceVertex()->getVertexIdx(),
edge->getTargetVertex()->getVertexIdx(),
edge->getSourceVertex()->getVertexIdx(),
edge->getTargetVertex()->getVertexIdx(), edge->getEdgeCost(),
baseLPMSTSolution.at(edge->getSourceVertex()->getVertexIdx()).at(
edge->getTargetVertex()->getVertexIdx()) ? '1' : '0');
goalFormula +=
edge->getEdgeCost()
* baseLPMSTSolution.at(
edge->getSourceVertex()->getVertexIdx()).at(
edge->getTargetVertex()->getVertexIdx());
}
INFO_NOARG(logger, LogBundleKey::CPLPIMST2_BUILD_MODEL_GOAL_INCR_CONSTRAINT);
graph->beginEdge();
while (graph->hasNextEdge(Connectivity::CONNECTED, Visibility::VISIBLE)) {
edge = graph->nextEdge();
INFO(logger, LogBundleKey::CPLPIMST2_BUILD_MODEL_GOAL_INCR_ADD,
edge->getSourceVertex()->getVertexIdx(),
edge->getTargetVertex()->getVertexIdx(),
edge->getSourceVertex()->getVertexIdx(),
edge->getTargetVertex()->getVertexIdx(),
edge->getSourceVertex()->getVertexIdx(),
edge->getTargetVertex()->getVertexIdx(), edge->getEdgeCost(),
getVariableName(getAddEdgeVariable(edge)).c_str(),
getVariableName(getDropEdgeVariable(edge)).c_str());
goalFormula += edge->getEdgeCost()
* (getAddEdgeVariable(edge) - getDropEdgeVariable(edge));
}
model.add(IloMinimize(env, goalFormula));
}
int
main(int argc, char **argv)
{
IloEnv env;
try {
IloInt i, j;
IloNum rollWidth;
IloNumArray amount(env);
IloNumArray size(env);
if ( argc > 1 )
readData(argv[1], rollWidth, size, amount);
else
readData("../../../examples/data/cutstock.dat",
rollWidth, size, amount);
/// CUTTING-OPTIMIZATION PROBLEM ///
IloModel cutOpt (env);
IloObjective RollsUsed = IloAdd(cutOpt, IloMinimize(env));
IloRangeArray Fill = IloAdd(cutOpt,
IloRangeArray(env, amount, IloInfinity));
IloNumVarArray Cut(env);
IloInt nWdth = size.getSize();
for (j = 0; j < nWdth; j++) {
Cut.add(IloNumVar(RollsUsed(1) + Fill[j](int(rollWidth / size[j]))));
}
IloCplex cutSolver(cutOpt);
/// PATTERN-GENERATION PROBLEM ///
IloModel patGen (env);
IloObjective ReducedCost = IloAdd(patGen, IloMinimize(env, 1));
IloNumVarArray Use(env, nWdth, 0.0, IloInfinity, ILOINT);
patGen.add(IloScalProd(size, Use) <= rollWidth);
IloCplex patSolver(patGen);
/// COLUMN-GENERATION PROCEDURE ///
IloNumArray price(env, nWdth);
IloNumArray newPatt(env, nWdth);
/// COLUMN-GENERATION PROCEDURE ///
for (;;) {
/// OPTIMIZE OVER CURRENT PATTERNS ///
cutSolver.solve();
report1 (cutSolver, Cut, Fill);
/// FIND AND ADD A NEW PATTERN ///
for (i = 0; i < nWdth; i++) {
price[i] = -cutSolver.getDual(Fill[i]);
}
ReducedCost.setLinearCoefs(Use, price);
patSolver.solve();
report2 (patSolver, Use, ReducedCost);
if (patSolver.getValue(ReducedCost) > -RC_EPS) break;
patSolver.getValues(newPatt, Use);
Cut.add( IloNumVar(RollsUsed(1) + Fill(newPatt)) );
}
cutOpt.add(IloConversion(env, Cut, ILOINT));
cutSolver.solve();
cout << "Solution status: " << cutSolver.getStatus() << endl;
report3 (cutSolver, Cut);
}
catch (IloException& ex) {
cerr << "Error: " << ex << endl;
}
catch (...) {
cerr << "Error" << endl;
}
env.end();
return 0;
}
int CProblem::setModel()
{
//time_t start, end;
IloEnv env;
try
{
IloModel model(env);
IloCplex cplex(env);
/*Variables*/
IloNumVar lambda(env, "lambda");
IloNumVarArray c(env, n); //
for (unsigned int u = 0; u < n; u++)
{
std::stringstream ss;
ss << u;
std::string str = "c" + ss.str();
c[u] = IloNumVar(env, str.c_str());
}
IloIntVarArray z0(env, Info.numAddVar);
for (int i = 0; i < Info.numAddVar; i++)
{
std::stringstream ss;
ss << i;
std::string str = "z0_" + ss.str();
z0[i] = IloIntVar(env, 0, 1, str.c_str());
}
/* Objective*/
model.add(IloMinimize(env, lambda));
/*Constrains*/
/* d=function of the distance */
IloArray<IloNumArray> Par_d(env, n);
for (unsigned int u = 0; u < n; u++)
{
Par_d[u] = IloNumArray(env, n);
for (unsigned int v = 0; v < n; v++)
{
Par_d[u][v] = d[u][v];
}
}
int M = (max_distance + 1) * n;
for (int i = 0; i < Info.numAddVar; i++)
{
int u = Info.ConstrIndex[i * 2];
int v = Info.ConstrIndex[i * 2 + 1];
model.add(c[u] - c[v] + M * (z0[i]) >= Par_d[u][v]);
model.add(c[v] - c[u] + M * (1 - z0[i]) >= Par_d[u][v]);
}
// d(x) = 3 - x
if (max_distance == 2)
{
// Gridgraphen 1x2 (6 Knoten)
for (int i = 0; i < sqrt(n)-1; i+=1)
{
for (int j = 0; j < sqrt(n)-2; j+=2)
{
model.add(c[i * sqrt(n) + j] + c[i * sqrt(n) + j+2] +
c[(i+1) * sqrt(n) + j] + c[(i+1) * sqrt(n) + j+2] >= 16.2273);
}
}
//
}
//d(x) = 4 - x
if (max_distance == 3)
{
// Gridgraphen 1x2 (6 Knoten)
for (int i = 0; i < sqrt(n)-1; i+=1)
{
for (int j = 0; j < sqrt(n)-2; j+=2)
{
model.add(c[i * sqrt(n) + j] + c[i * sqrt(n) + j+2] +
c[(i+1) * sqrt(n) + j] + c[(i+1) * sqrt(n) + j+2] >= 30.283);
}
}
}
for (unsigned int v = 0; v < n; v++)
{
model.add(c[v] <= lambda);
model.add(c[v] >= 0);
}
std::cout << "Number of variables " << Info.numVar << "\n";
/* solve the Model*/
cplex.extract(model);
cplex.exportModel("L-Labeling.lp");
IloTimer timer(env);
timer.start();
int solveError = cplex.solve();
timer.stop();
if (!solveError)
{
std::cout << "STATUS : " << cplex.getStatus() << "\n";
env.error() << "Failed to optimize LP \n";
exit(1);
}
//Info.time = (double)(end-start)/(double)CLOCKS_PER_SEC;
Info.time = timer.getTime();
std::cout << "STATUS : " << cplex.getStatus() << "\n";
/* get the solution*/
env.out() << "Solution status = " << cplex.getStatus() << "\n";
Info.numConstr = cplex.getNrows();
env.out() << " Number of constraints = " << Info.numConstr << "\n";
lambda_G_d = cplex.getObjValue();
env.out() << "Solution value = " << lambda_G_d << "\n";
for (unsigned int u = 0; u < n; u++)
{
C.push_back(cplex.getValue(c[u]));
std::cout << "c(" << u << ")=" << C[u] << " ";
}
} // end try
catch (IloException& e)
{
std::cerr << "Concert exception caught: " << e << std::endl;
} catch (...)
{
std::cerr << "Unknown exception caught" << std::endl;
}
env.end();
return 0;
}
int main(int argc, const char* argv[]){
IloEnv env;
try {
const char* filename = "../../../examples/data/flowshop_default.data";
IloInt failLimit = IloIntMax;
if (argc > 1)
filename = argv[1];
if (argc > 2)
failLimit = atoi(argv[2]);
std::ifstream file(filename);
if (!file){
env.out() << "usage: " << argv[0] << " <file> <failLimit>" << std::endl;
throw FileError();
}
IloInt i,j;
IloModel model(env);
IloInt nbJobs, nbMachines;
file >> nbJobs;
file >> nbMachines;
IloIntervalVarArray2 machines(env, nbMachines);
for (j = 0; j < nbMachines; j++)
machines[j] = IloIntervalVarArray(env);
IloIntExprArray ends(env);
for (i = 0; i < nbJobs; i++) {
IloIntervalVar prec;
for (j = 0; j < nbMachines; j++) {
IloInt d;
file >> d;
IloIntervalVar ti(env, d);
machines[j].add(ti);
if (0 != prec.getImpl())
model.add(IloEndBeforeStart(env, prec, ti));
prec = ti;
}
ends.add(IloEndOf(prec));
}
IloIntervalSequenceVarArray seqs(env,nbMachines);
for (j = 0; j < nbMachines; j++) {
seqs[j] = IloIntervalSequenceVar(env, machines[j]);
model.add(IloNoOverlap(env,seqs[j]));
if (0<j) {
model.add(IloSameSequence(env, seqs[0],seqs[j]));
}
}
IloObjective objective = IloMinimize(env,IloMax(ends));
model.add(objective);
IloCP cp(model);
cp.setParameter(IloCP::FailLimit, failLimit);
cp.setParameter(IloCP::LogPeriod, 10000);
cp.out() << "Instance \t: " << filename << std::endl;
if (cp.solve()) {
cp.out() << "Makespan \t: " << cp.getObjValue() << std::endl;
} else {
cp.out() << "No solution found." << std::endl;
}
} catch(IloException& e){
env.out() << " ERROR: " << e << std::endl;
}
env.end();
return 0;
}
int
main(int, char**)
{
IloEnv env;
try {
IloInt j;
define_data(env);
IloModel model(env);
IloNumVarArray m(env, nbElements, 0.0, IloInfinity);
IloNumVarArray r(env, nbRaw, 0.0, IloInfinity);
IloNumVarArray s(env, nbScrap, 0.0, IloInfinity);
IloNumVarArray i(env, nbIngot, 0, 100000, ILOINT);
IloNumVarArray e(env, nbElements);
// Objective Function: Minimize Cost
model.add(IloMinimize(env, IloScalProd(nm, m) + IloScalProd(nr, r) +
IloScalProd(ns, s) + IloScalProd(ni, i) ));
// Min and max quantity of each element in alloy
for (j = 0; j < nbElements; j++)
e[j] = IloNumVar(env, p[j] * alloy, P[j] * alloy);
// Constraint: produce requested quantity of alloy
model.add(IloSum(e) == alloy);
// Constraints: Satisfy element quantity requirements for alloy
for (j = 0; j < nbElements; j++) {
model.add(e[j] == m[j] + IloScalProd(PRaw[j], r)
+ IloScalProd(PScrap[j], s)
+ IloScalProd(PIngot[j], i));
}
// Optimize
IloCplex cplex(model);
cplex.setOut(env.getNullStream());
cplex.setWarning(env.getNullStream());
cplex.solve();
if (cplex.getStatus() == IloAlgorithm::Infeasible)
env.out() << "No Solution" << endl;
env.out() << "Solution status: " << cplex.getStatus() << endl;
// Print results
env.out() << "Cost:" << cplex.getObjValue() << endl;
env.out() << "Pure metal:" << endl;
for(j = 0; j < nbElements; j++)
env.out() << j << ") " << cplex.getValue(m[j]) << endl;
env.out() << "Raw material:" << endl;
for(j = 0; j < nbRaw; j++)
env.out() << j << ") " << cplex.getValue(r[j]) << endl;
env.out() << "Scrap:" << endl;
for(j = 0; j < nbScrap; j++)
env.out() << j << ") " << cplex.getValue(s[j]) << endl;
env.out() << "Ingots : " << endl;
for(j = 0; j < nbIngot; j++)
env.out() << j << ") " << cplex.getValue(i[j]) << endl;
env.out() << "Elements:" << endl;
for(j = 0; j < nbElements; j++)
env.out() << j << ") " << cplex.getValue(e[j]) << endl;
}
catch (IloException& ex) {
cerr << "Error: " << ex << endl;
}
catch (...) {
cerr << "Error" << endl;
}
env.end();
return 0;
}
int CProblem::setModel() {
//time_t start, end;
numvar = 1+n; // lambda + all c;
IloEnv env;
try {
IloModel model(env);
IloCplex cplex(env);
/*Variables*/
IloNumVar lambda(env, "lambda");
IloNumVarArray c(env, n);//
for (unsigned int u=0; u<n; u++) {
std::stringstream ss;
ss << u;
std::string str = "c" + ss.str();
c[u]=IloNumVar(env, str.c_str());
}
IloArray<IloIntVarArray> z(env,n);
for (unsigned int u=0; u<n; u++) {
z[u]= IloIntVarArray(env, n);
for (unsigned int v=0; v<n; v++) {
std::stringstream ss;
ss << u;
ss << v;
std::string str = "z" + ss.str();
z[u][v] = IloIntVar(env, 0, 1, str.c_str());
}
}
/* Constant M*/
int M=n*max_d;
UB = M;
/* Objective*/
model.add(IloMinimize(env, lambda));
/*Constrains*/
model.add(lambda - UB <= 0);
/* d=function of the distance */
IloArray<IloNumArray> Par_d(env,n);
for (unsigned int u=0; u<n; u++) {
Par_d[u]=IloNumArray(env,n);
for (unsigned int v=0; v<n; v++) {
Par_d[u][v]=d[u][v];
}
}
for (unsigned u=0; u<n; u++) {
for (unsigned v=0; v<u; v++) {
model.add(c[v]-c[u]+M* z[u][v] >= Par_d[u][v] );
model.add(c[u]-c[v]+M*(1-z[u][v]) >= Par_d[u][v]);
numvar++; // + z[u][v]
}
}
for (unsigned int v=0; v<n; v++) {
IloExpr expr;
model.add (c[v] <= lambda);
model.add (c[v] >= 0);
expr.end();
}
std::cout << "Number of variables " << numvar << "\n";
/* solve the Model*/
cplex.extract(model);
cplex.exportModel("L-Labeling.lp");
/*
start = clock();
int solveError = cplex.solve();
end = clock ();
*/
// cplex.setParam(IloCplex::Threads, 1);
cplex.setParam(IloCplex::ClockType , 1 ); // CPU time
IloTimer timer(env);
const double startt = cplex.getTime();
timer.start();
int solveError = cplex.solve();
timer.stop();
const double stopt = cplex.getTime() - startt;
if ( !solveError ) {
std::cout << "STATUS : "<< cplex.getStatus() << "\n";
env.error() << "Failed to optimize LP \n";
exit(1);
}
//Info.time = (double)(end-start)/(double)CLOCKS_PER_SEC;
Info.time = timer.getTime();
std::cout << "STATUS : "<< cplex.getStatus() << "\n";
/* get the solution*/
env.out() << "Solution status = " << cplex.getStatus() << "\n";
env.out() << "Time cplex.getTime " << stopt << "\n";
numconst = cplex.getNrows();
env.out() << " Number of constraints = " << numconst << "\n";
lambda_G_d=cplex.getObjValue();
env.out() << "Solution value = " << lambda_G_d << "\n";
for (unsigned int u=0; u<n; u++) {
C.push_back(cplex.getValue(c[u]));
std::cout << "c(" << u << ")=" << C[u]<< " ";
}
std::cout <<"\n";
/*
for (unsigned int u=0; u<n; u++) {
for (unsigned int v=0; v<u; v++) {
std::cout<< "z[" << u <<"][" << v << "]="<< cplex.getValue( z[u][v]) << " ";
Z.push_back(cplex.getValue( z[u][v]));
}
std::cout << "\n";
}
std::cout <<"\n";
*/
} // end try
catch (IloException& e) {
std::cerr << "Concert exception caught: " << e << std::endl;
}
catch (...) {
std::cerr << "Unknown exception caught" << std::endl;
}
env.end();
return 0;
}
int main (int argc, char *argv[])
{
ifstream infile;
infile.open("Proj3_op.txt");
if(!infile){
cerr << "Unable to open the file\n";
exit(1);
}
cout << "Before Everything!!!" << "\n";
IloEnv env;
IloInt i,j,varCount1,varCount2,varCount3,conCount; //same as “int i;”
IloInt k,w,K,W,E,l,P,N,L;
IloInt tab, newline, val; //from file
char line[2048];
try {
N = 9;
K = 3;
L = 36;
W = (IloInt)atoi(argv[1]);
IloModel model(env); //set up a model object
IloNumVarArray var1(env);// C - primary
cout << "Here\n";
IloNumVarArray var2(env);// B - backup
cout << "here1\n";
IloNumVarArray var3(env);// = IloNumVarArray(env,W); //declare an array of variable objects, for unknowns
IloNumVar W_max(env, 0, W, ILOINT);
//var1: c_ijk_w
IloRangeArray con(env);// = IloRangeArray(env,N*N + 3*w); //declare an array of constraint objects
IloNumArray2 t = IloNumArray2(env,N); //Traffic Demand
IloNumArray2 e = IloNumArray2(env,N); //edge matrix
//IloObjective obj;
//Define the Xijk matrix
cout << "Before init xijkl\n";
Xijkl xijkl_m(env, L);
for(l=0;l<L;l++){
xijkl_m[l] = Xijk(env, N);
for(i=0;i<N;i++){
xijkl_m[l][i] = Xjk(env, N);
for(j=0;j<N;j++){
xijkl_m[l][i][j] = IloNumArray(env, K);
}
}
}
//reset everything to zero here
for(l=0;l<L;l++)
for(i=0;i<N;i++)
for(j=0;j<N;j++)
for(k=0;k<K;k++)
xijkl_m[l][i][j][k] = 0;
input_Xijkl(xijkl_m);
cout<<"bahre\n";
for(i=0;i<N;i++){
t[i] = IloNumArray(env,N);
for(j=0;j<N;j++){
if(i == j)
t[i][j] = IloNum(0);
else if(i != j)
t[i][j] = IloNum((i+j+2)%5);
}
}
printf("ikde\n");
//Minimize W_max
IloObjective obj=IloMinimize(env);
obj.setLinearCoef(W_max, 1.0);
cout << "here khali\n";
//Setting var1[] for Demands Constraints
for(i=0;i<N;i++)
for(j=0;j<N;j++)
for(k=0;k<K;k++)
for(w=0;w<W;w++)
var1.add(IloNumVar(env, 0, 1, ILOINT));
//c_ijk_w variables set.
//Setting var2[] for Demands Constraints
for(i=0;i<N;i++)
for(j=0;j<N;j++)
for(k=0;k<K;k++)
for(w=0;w<W;w++)
var2.add(IloNumVar(env, 0, 1, ILOINT));
//b_ijk_w variables set.
for(w = 0;w < W;w++)
var3.add(IloNumVar(env, 0, 1, ILOINT)); //Variables for u_w
cout<<"variables ready\n";
conCount = 0;
for(i=0;i<N;i++)
for(j=0;j<N;j++){
con.add(IloRange(env, 2 * t[i][j], 2 * t[i][j]));
//varCount1 = 0;
for(k=0;k<K;k++)
for(w=0;w<W;w++){
con[conCount].setLinearCoef(var1[i*N*W*K+j*W*K+k*W+w],1.0);
con[conCount].setLinearCoef(var2[i*N*W*K+j*W*K+k*W+w],1.0);
}
conCount++;
}//Adding Demands Constraints to con
cout<<"1st\n";
IloInt z= 0;
for(w=0;w<W;w++){
for(l=0;l<L;l++){
con.add(IloRange(env, -IloInfinity, 1));
for(i=0;i<N;i++){
for(j=0;j<N;j++){
for(k=0;k<K;k++){
con[conCount].setLinearCoef(var1[i*N*W*K+j*W*K+k*W+w],xijkl_m[l][i][j][k]);
con[conCount].setLinearCoef(var2[i*N*W*K+j*W*K+k*W+w],xijkl_m[l][i][j][k]);
}
}
}
conCount++;
}
}
cout<<"2nd\n";
//Adding Wavelength Constraints_1 to con
P = N * (N-1) * K;
for(w=0;w<W;w++){
con.add(IloRange(env, -IloInfinity, 0));
varCount1 = 0;
for(i=0;i<N;i++)
for(j=0;j<N;j++)
for(k=0;k<K;k++){
con[conCount].setLinearCoef(var1[i*N*W*K+j*W*K+k*W+w],1.0);
con[conCount].setLinearCoef(var2[i*N*W*K+j*W*K+k*W+w],1.0);
}
con[conCount].setLinearCoef(var3[w],-P);
conCount++;
}
cout<<"3rd\n";
for(i=0;i<N;i++)
for(j=0;j<N;j++)
for(k=0;k<K;k++)
for(w=0;w<W;w++){
con.add(IloRange(env, -IloInfinity, 1));
con[conCount].setLinearCoef(var1[i*N*W*K+j*W*K+k*W+w], 1.0);
con[conCount++].setLinearCoef(var2[i*N*W*K+j*W*K+k*W+w], 1.0);
}
varCount3 = 0;
for(w=0;w<W;w++){
con.add(IloRange(env, 0, IloInfinity));
con[conCount].setLinearCoef(W_max, 1.0);
con[conCount++].setLinearCoef(var3[w], -1.0 * (w+1));
}
cout<<"after constraints\n";
//model.add(obj); //add objective function into model
model.add(IloMinimize(env,obj));
model.add(con); //add constraints into model
IloCplex cplex(model); //create a cplex object and extract the //model to this cplex object
// Optimize the problem and obtain solution.
if ( !cplex.solve() ) {
env.error() << "Failed to optimize LP" << endl;
throw(-1);
}
IloNumArray vals(env); //declare an array to store the outputs
//if 2 dimensional: IloNumArray2 vals(env);
env.out() << "Solution status = " << cplex.getStatus() << endl;
//return the status: Feasible/Optimal/Infeasible/Unbounded/Error/…
env.out() << "Solution value = " << cplex.getObjValue() << endl;
//return the optimal value for objective function
cplex.getValues(vals, var1); //get the variable outputs
env.out() << "Values Var1 = " << vals << endl; //env.out() : output stream
cplex.getValues(vals, var3);
env.out() << "Values Val3 = " << vals << endl;
}
catch (IloException& e) {
cerr << "Concert exception caught: " << e << endl;
}
catch (...) {
cerr << "Unknown exception caught" << endl;
}
env.end(); //close the CPLEX environment
return 0;
} // END main
void LpSolver::addObjective(string mode,
CplexConverter& cplexConverter, IloModel model,
IloNumVarArray x, IloRangeArray c){
// cerr << mode << endl;
IloEnv env = model.getEnv();
if (mode == "MIN_SRC_COST"){
IloExpr cost(env);
// add cost to all atomic edges
for (int i = 0; i < cplexConverter.variables.size(); ++i){
cost += x[i] * cplexConverter.variables[i].interest_rate;
}
for(auto &atoIn : cplexConverter.src->atomicEdge_in){
int aeId = atoIn.second->atomicEdgeId;
for (int j = 0; j < cplexConverter.atomicIdToVarIdDict[aeId].size(); j++){
// var Id
int vId = cplexConverter.atomicIdToVarIdDict[aeId][j];
cost += cplexConverter.variables[vId].interest_rate * x[vId];
// cout << "adding " << cplexConverter.variables[vId].interest_rate
// << " * " << vId << endl;
}
}
model.add(IloMinimize(env, cost));
} else if (mode == "MIN_CREDIT_COST") {
IloExpr cost(env);
// add cost to all atomic edges
for (int i = 0; i < cplexConverter.variables.size(); ++i){
cost += x[i] * cplexConverter.variables[i].interest_rate;
}
for (int i = 0; i < cplexConverter.variables.size(); ++i){
int aeId = cplexConverter.variables[i].atomicEdgeId;
if (!cplexConverter.graph->atomicEdges[aeId]->isDebt){
cost += x[i];
}
}
model.add(IloMinimize(env, cost));
} else if (mode == "MIN_CREDIT_SRC") {
IloExpr cost(env);
for(auto &atoIn : cplexConverter.src->atomicEdge_in){
int aeId = atoIn.second->atomicEdgeId;
for (int j = 0; j < cplexConverter.atomicIdToVarIdDict[aeId].size(); j++){
// var Id
int vId = cplexConverter.atomicIdToVarIdDict[aeId][j];
cost += cplexConverter.variables[vId].interest_rate * x[vId];
if (!cplexConverter.graph->atomicEdges[aeId]->isDebt){
cost += x[vId];
}
// cout << "adding " << cplexConverter.variables[vId].interest_rate
// << " * " << vId << endl;
}
}
model.add(IloMinimize(env, cost));
} else if (mode == "MIN_DEGREE_SRC") {
IloExpr cost(env);
for(auto &atoIn : cplexConverter.src->atomicEdge_in){
int aeId = atoIn.second->atomicEdgeId;
AtomicEdge* atEdge = cplexConverter.graph->atomicEdges[aeId];
for (int j = 0; j < cplexConverter.atomicIdToVarIdDict[aeId].size(); j++){
// var Id
int vId = cplexConverter.atomicIdToVarIdDict[aeId][j];
cost += cplexConverter.variables[vId].interest_rate * x[vId];
if (!atEdge->isDebt){
cost += atEdge->nodeTo->degree * x[vId];
} else {
cost += atEdge->nodeFrom->degree * x[vId];
}
// cout << "adding " << cplexConverter.variables[vId].interest_rate
// << " * " << vId << endl;
}
}
model.add(IloMinimize(env, cost));
}
else if (mode == "MAX_IR_COST") {
IloExpr cost(env);
// add cost to all atomic edges
for (int i = 0; i < cplexConverter.variables.size(); ++i){
cost += 100 - x[i] * cplexConverter.variables[i].interest_rate;
}
model.add(IloMinimize(env, cost));
} else if (mode == "MIN_SUMIR_COST") {
IloExpr cost(env);
// add cost to all atomic edges
for (int i = 0; i < cplexConverter.variables.size(); ++i){
cost += x[i] * cplexConverter.variables[i].interest_rate;
}
model.add(IloMinimize(env, cost));
} else if (mode == "MIN_DEGREE_COST") {
IloExpr cost(env);
// add cost to all atomic edges
for (int i = 0; i < cplexConverter.variables.size(); ++i){
cost += x[i] * cplexConverter.variables[i].interest_rate;
}
for (int i = 0; i < cplexConverter.variables.size(); ++i){
int aeId = cplexConverter.variables[i].atomicEdgeId;
AtomicEdge* atEdge = cplexConverter.graph->atomicEdges[aeId];
for (int j = 0; j < cplexConverter.atomicIdToVarIdDict[aeId].size(); j++){
// var Id
int vId = cplexConverter.atomicIdToVarIdDict[aeId][j];
if (!atEdge->isDebt){
cost += atEdge->nodeTo->degree * x[vId];
} else {
cost += atEdge->nodeFrom->degree * x[vId];
}
}
}
model.add(IloMinimize(env, cost));
} else {
// default
model.add(IloMinimize(env, 1));
}
}
static void populatebyrow (IloModel model, IloNumVarArray x, IloRangeArray c) {
IloEnv env = model.getEnv();
IloNumArray costs(env);
IloNumArray time(env);
IloNumArray product(env);
int costs_array[] = {1,1,1,10,1,12,2,2,5,10};
int time_array[] = {10,1,7,3,2,3,2,3,7,1};
int product_array[] = {0,3,1,2,-2,0,0,0,0,0};
for(int i=0;i<10;i++)
costs.add(costs_array[i]);
for(int i=0;i<10;i++)
time.add(time_array[i]);
for(int i=0;i<10;i++)
product.add(product_array[i]);
x.add(IloBoolVar(env,"x12")); //0
x.add(IloBoolVar(env,"x24")); //1
x.add(IloBoolVar(env,"x46")); //2
x.add(IloBoolVar(env,"x13")); //3
x.add(IloBoolVar(env,"x32")); //4
x.add(IloBoolVar(env,"x35")); //5
x.add(IloBoolVar(env,"x56")); //6
x.add(IloBoolVar(env,"x25")); //7
x.add(IloBoolVar(env,"x34")); //8
x.add(IloBoolVar(env,"x45")); //9
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"s2")); //10
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"s3")); //11
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"s4")); //12
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"s5")); //13
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"s1")); //14
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"s6")); //15
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"q2")); //16
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"q3")); //17
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"q4")); //18
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"q5")); //19
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"q1")); //20
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"q6")); //21
model.add(IloMinimize(env, costs[0]*x[0] + costs[1]*x[1] + costs[2]*x[2] + costs[3]*x[3] + costs[4]*x[4] + costs[5]*x[5] + costs[6]*x[6] + costs[7]*x[7] + costs[8]*x[8] + costs[9]*x[9]));
c.add(x[0]+ x[3] == 1); // arcs sortant du noeud de depart
c.add(x[2]+ x[6] == 1); // arcs entrant au noeud d arrivee
c.add(x[1]+ x[7] - x[0] - x[4] == 0);
c.add(x[8]+ x[5] + x[4] - x[3] == 0);
c.add(x[9]+ x[2] - x[1] - x[8] == 0);
c.add(x[6]- x[7] - x[5] - x[9] == 0);
c.add(time[0]*x[0] + time[1]*x[1] + time[2]*x[2] + time[3]*x[3] + time[4]*x[4] + time[5]*x[5] + time[6]*x[6] + time[7]*x[7] + time[8]*x[8] + time[9]*x[9] <= 14);
//c.add(product[0]*x[0] + product[1]*x[1] + product[2]*x[2] + product[3]*x[3] + product[4]*x[4] + product[5]*x[5] + product[6]*x[6] + product[7]*x[7] + product[8]*x[8] + product[9]*x[9] <= 4);
c.add(x[14]+time[0]-1000*(1-x[0]) - x[10]<= 0);
c.add(x[20]+product[0]-1000*(1-x[0]) - x[16]<= 0);
c.add(x[10]+time[1]-1000*(1-x[1]) - x[12]<= 0);
c.add(x[16]+product[1]-1000*(1-x[1]) - x[18]<= 0);
c.add(x[18]-product[1]-1000*(1-x[1]) - x[16]<= 0);
c.add(x[12]+time[2]-1000*(1-x[2]) - x[15]<= 0);
c.add(x[18]+product[2]-1000*(1-x[2]) - x[21]<= 0);
c.add(x[21]-product[2]-1000*(1-x[2]) - x[18]<= 0);
c.add(x[14]+time[3]-1000*(1-x[3]) - x[11]<= 0);
c.add(x[20]+product[3]-1000*(1-x[3]) - x[17]<= 0);
c.add(x[17]-product[3]-1000*(1-x[3]) - x[20]<= 0);
c.add(x[13]+time[6]-1000*(1-x[6]) - x[15]<= 0);
c.add(x[19]+product[6]-1000*(1-x[6]) - x[21]<= 0);
c.add(x[10]+time[7]-1000*(1-x[7]) - x[13]<= 0);
c.add(x[16]+product[7]-1000*(1-x[7]) - x[19]<= 0);
c.add(x[12]+time[9]-1000*(1-x[9]) - x[13]<= 0);
c.add(x[18]+product[9]-1000*(1-x[9]) - x[19]<= 0);
c.add(x[11]+time[4]-1000*(1-x[4]) - x[10] <=0);
c.add(x[17]+product[4]-1000*(1-x[4]) - x[16]<= 0);
c.add(x[16]-product[4]-1000*(1-x[4]) - x[17]<= 0);
c.add(x[11]+time[8]-1000*(1-x[8]) - x[12]<= 0);
c.add(x[17]+product[8]-1000*(1-x[8]) - x[18]<= 0);
c.add(x[11]+time[5]-1000*(1-x[5]) - x[13]<= 0);
c.add(x[17]+product[5]-1000*(1-x[5]) - x[19]<= 0);
c.add(5 <= x[10] <= 7);
c.add(2 <= x[11] <= 5);
c.add(5 <= x[12] <= 9);
c.add(0 <= x[13] <= 20);
c.add(0 <= x[14] <= 0);
c.add(0 <= x[15] <= 14);
/*
c.add(2 <= x[17] <= 4);
c.add(0 <= x[16] <= 2);
c.add(3 <= x[18] <= 4);
c.add(0 <= x[19] <= 1000);
//c.add(0 <= x[20] <= 0);*/
c.add(3 <= x[21] <= 4);
c.add( x[20] == 1);
model.add(c);
}
ILOSTLBEGIN
vector<double> solve_qp_cplex(QP* qp, bool& success)
{
vector<double> res(qp->num_var, 0);
IloEnv env;
try {
IloModel model(env);
// variables
IloNumVarArray var(env);
for (int i=0; i<qp->num_var; i++)
{
if (qp->binary_idx[i]==0)
var.add(IloNumVar(env, qp->lb[i], qp->ub[i]));
else
var.add(IloNumVar(env, qp->lb[i], qp->ub[i], ILOINT));
}
// constraints
IloRangeArray con(env);
//TODO remove these fake constraints
for (int i=0; i<qp->num_var; i++)
{
con.add( var[i]<= 1e10);
}
// iterate over constraint matrix
for (int i=0; i<qp->b.size(); i++)
{
vector<int>* idx = NULL;
vector<float>* coef = NULL;
int ret = qp->A.get(&idx, &coef, i);
assert(idx!=NULL);
assert(coef!=NULL);
assert(ret>0);
assert(idx->size()==coef->size());
IloNumExpr c_expr(env);
for (int j=0; j<idx->size(); j++)
{
//printf("j:%i %.3f %i (num_var: %i)\n", j, coef->at(j), idx->at(j), qp->num_var);
assert(idx->at(j)<qp->num_var);
c_expr+= coef->at(j)*var[idx->at(j)];
}
if (qp->eq_idx[i]>0)
{
con.add(c_expr == qp->b[i]);
//env.out() << i << c_expr << "==" << qp->b[i] << endl;
}
else
{
con.add(c_expr <= qp->b[i]);
//env.out() << i << c_expr << "<=" << qp->b[i] << endl;
}
}
// objective
IloNumExpr obj_expr(env);
qp->Q.reset_it();
while (true)
{
int i=0;
int j=0;
float val = qp->Q.next(&i, &j);
//printf("%i %i %.2f\n", i, j, val);
if (i==-1)
break;
assert(i<qp->num_var && j<qp->num_var);
obj_expr+= var[i] * var[j] * val;
}
for (int i=0; i<qp->F.size(); i++)
{
if (qp->F[i]!=0)
obj_expr+= var[i] * qp->F[i];
}
IloObjective obj(env, IloMinimize(env, obj_expr));
//model.add(IloMinimize(env, var[0] + 2 * var[1] + 3 * var[2] + var[3]));
//env.out() << "objective: " << IloMinimize(env, obj_expr) << endl;
//env.out() << "constraints: " << con << endl;
// create model
//model.add(var);
model.add(con);
model.add(obj);
// solve...
IloCplex cplex(model);
cplex.solve();
IloNumArray vals(env);
cplex.getValues(vals, var);
//env.out() << "Values = " << vals << endl;
//cplex.getSlacks(vals, con);
//env.out() << "Slacks = " << vals << endl;
env.out() << "Solution status = " << cplex.getStatus() << endl;
env.out() << "Solution value = " << cplex.getObjValue() << endl;
for (int i=0; i<qp->num_var; i++)
res[i] = vals[i];
}
catch (IloException& e)
{
cerr << "Concert exception caught: " << e << endl;
success = false;
}
catch (...)
{
cerr << "Unknown exception caught" << endl;
success = false;
}
env.end();
return res;
}
/**************************************************程序入口****************************************************/
int main()
{
clock_t start,finish;
double totaltime;
start=clock();
try
{
define_data(env);//首先初始化全局变量
IloInvert(env,B0,B0l,Node-1);//求逆矩阵
/*************************************************************主问题目标函数*******************************************************/
IloNumExpr Cost(env);
for(IloInt w=0;w<NW;++w)
{
Cost+=detaPw[w];
}
Master_Model.add(IloMinimize(env,Cost));
//Master_Model.add(IloMaximize(env,Cost));//目标函数二先一
Cost.end();
/********************************************************机组出力上下限约束**************************************************/
IloNumExpr expr1(env),expr2(env);
for(IloInt i=0;i<NG;++i)
{
expr1+=detaP[i];
}
for(IloInt w=0;w<NW;++w)
{
expr2+=detaPw[w];
}
Master_Model.add(expr1==expr2);
expr1.end();
expr2.end();
for(IloInt i=0;i<NG;++i)
{
Master_Model.add(detaP[i]>=Unit[i][1]*u[i]-P1[i]);
Master_Model.add(detaP[i]<=Unit[i][2]*u[i]-P1[i]);
Master_Model.add(detaP[i]>=-detaa[i]);
Master_Model.add(detaP[i]<=detaa[i]);
}
IloNumExprArray detaP_node(env,Node-1),detaPw_node(env,Node-1);
IloNumExprArray Theta(env,Node);
for(IloInt b=0;b<Node-1;++b)
{
detaP_node[b]=IloNumExpr(env);
detaPw_node[b]=IloNumExpr(env);
IloInt i=0;
for(;i<NG;++i)
{
if(Unit[i][0]==b-1)break;
}
if(i<NG)
{
detaP_node[b]+=detaP[i];
}
else
{
detaP_node[b]+=0;
}
if(Sw[b]>=0)
{
detaPw_node[b]+=detaPw[ Sw[b] ];
}
else
{
detaPw_node[b]+=0;
}
}
for(IloInt b=0;b<Node-1;++b)
{
Theta[b]=IloNumExpr(env);
for(IloInt k=0;k<Node-1;++k)
{
Theta[b]+=B0l[b][k]*(detaP_node[k]+detaPw_node[k]);
}
}
Theta[Node-1]=IloNumExpr(env);
for(IloInt h=0;h<Branch;++h)
{
IloNumExpr exprTheta(env);//莫明其妙的错误
exprTheta+=(Theta[(IloInt)Info_Branch[h][0]-1]-Theta[(IloInt)Info_Branch[h][1]-1]);
Master_Model.add(exprTheta<=Info_Branch[h][3]*(Info_Branch[h][4]-PL[h]));
Master_Model.add(exprTheta>=Info_Branch[h][3]*(-Info_Branch[h][4]-PL[h]));
exprTheta.end();
//两个相减的节点顺序没有影响么？
}
Theta.end();
detaP_node.end();
detaPw_node.end();
Master_Cplex.extract(Master_Model);
Master_Cplex.solve();
if (Master_Cplex.getStatus() == IloAlgorithm::Infeasible)//输出结果
{
output<<"Master Problem Have No Solution"<<endl;
goto lable2;
}
/************************************************************输出显示过程**************************************************/
output<<endl<<"Min/Max:"<<Master_Cplex.getObjValue()<<endl;
lable2:
Master_Model.end();
Master_Cplex.end();
env.end();
}
catch(IloException& ex)//异常捕获
{
output<<"Error: "<<ex<<endl;
}
catch(...)
{
output<<"Error: Unknown exception caught!" << endl;
}
finish=clock();
totaltime=(double)(finish-start)/CLOCKS_PER_SEC;
output<<"totaltime: "<<totaltime<<"s"<<endl<<endl;
output.close();
return 0;
}
int main (int argc, char *argv[])
{
ifstream infile;
clock_t start_time, end_time;
infile.open("Proj3_op.txt");
if(!infile){
cerr << "Unable to open the file\n";
exit(1);
}
cout << "Before Everything!!!" << "\n";
IloEnv env;
IloInt i,j,varCount1,varCount2,varCount3,conCount; //same as “int i;”
IloInt k,w,K,W,E,l,P,N,L;
IloInt tab, newline, val; //from file
char line[2048];
try {
N = 9;
K = 2;
L = 36;
W = (IloInt)atoi(argv[1]);
IloModel model(env); //set up a model object
IloNumVarArray var1(env);// = IloNumVarArray(env,K*W*N*N);
IloNumVarArray var3(env);// = IloNumVarArray(env,W); //declare an array of variable objects, for unknowns
IloNumVar W_max(env, 0, W, ILOINT);
IloRangeArray con(env);// = IloRangeArray(env,N*N + 3*w); //declare an array of constraint objects
IloNumArray2 t = IloNumArray2(env,N); //Traffic Demand
IloNumArray2 e = IloNumArray2(env,N); //edge matrix
//IloObjective obj;
//Define the Xijk matrix
Xijkl xijkl_m(env, L);
for(l=0;l<L;l++){
xijkl_m[l] = Xijk(env, N);
for(i=0;i<N;i++){
xijkl_m[l][i] = Xjk(env, N);
for(j=0;j<N;j++){
xijkl_m[l][i][j] = IloNumArray(env, K);
}
}
}
//reset everything to zero here
for(l=0;l<L;l++)
for(i=0;i<N;i++)
for(j=0;j<N;j++)
for(k=0;k<K;k++)
xijkl_m[l][i][j][k] = 0;
input_Xijkl(xijkl_m);
cout<<"bahre\n";
FILE *file;
file = fopen(argv[2],"r");
int tj[10];
for(i=0;i<N;i++){
t[i] = IloNumArray(env,N);
fscanf(file,"%d %d %d %d %d %d %d %d %d \n",&tj[0],&tj[1],&tj[2],&tj[3],&tj[4],&tj[5],&tj[6],&tj[7],&tj[8]);
for(j=0;j<N;j++){
t[i][j] = IloNum(tj[j]);
}
}
printf("ikde\n");
//Minimize W_max
IloObjective obj=IloMinimize(env);
obj.setLinearCoef(W_max, 1.0);
cout << "here khali\n";
//Setting var1[] for Demands Constraints
for(i=0;i<N;i++)
for(j=0;j<N;j++)
for(k=0;k<K;k++)
for(w=0;w<W;w++)
var1.add(IloNumVar(env, 0, 1, ILOINT));
for(w = 0;w < W;w++)
var3.add(IloNumVar(env, 0, 1, ILOINT)); //Variables for u_w
cout<<"variables ready\n";
conCount = 0;
for(i=0;i<N;i++)
for(j=0;j<N;j++){
con.add(IloRange(env, t[i][j], t[i][j]));
//varCount1 = 0;
for(k=0;k<K;k++)
for(w=0;w<W;w++){
con[conCount].setLinearCoef(var1[i*N*W*K+j*W*K+k*W+w],1.0);
//cout << "Before Adding Constraint\n";
//con[1].setLinearCoef(IloNumVar(env, 0, 1, ILOINT), 1.0);
//cout<<"coef set "<<varCount1;
}
conCount++;
}//Adding Demands Constraints to con
cout<<"1st\n";
IloInt z= 0;
for(w=0;w<W;w++){
for(l=0;l<L;l++){
con.add(IloRange(env, -IloInfinity, 1));
for(i=0;i<N;i++){
for(j=0;j<N;j++){
for(k=0;k<K;k++){
con[conCount].setLinearCoef(var1[i*N*W*K+j*W*K+k*W+w],xijkl_m[l][i][j][k]);
}
}
}
conCount++;
}
}
cout<<"2nd\n";
//Adding Wavelength Constraints_1 to con
P = N * (N-1) * K;
for(w=0;w<W;w++){
con.add(IloRange(env, -IloInfinity, 0));
varCount1 = 0;
for(i=0;i<9;i++)
for(j=0;j<9;j++)
for(k=0;k<K;k++){
con[conCount].setLinearCoef(var1[i*N*W*K+j*W*K+k*W+w],1.0);
}
con[conCount].setLinearCoef(var3[w],-P);
conCount++;
}
cout<<"3rd\n";
varCount3 = 0;
for(w=0;w<W;w++){
con.add(IloRange(env, 0, IloInfinity));
con[conCount].setLinearCoef(W_max, 1.0);
con[conCount++].setLinearCoef(var3[w], -1.0 * (w+1));
}
cout<<"after constraints\n";
//model.add(obj); //add objective function into model
model.add(IloMinimize(env,obj));
model.add(con); //add constraints into model
IloCplex cplex(model); //create a cplex object and extract the //model to this cplex object
// Optimize the problem and obtain solution.
start_time = clock();
if ( !cplex.solve() ) {
env.error() << "Failed to optimize LP" << endl;
throw(-1);
}
end_time = clock();
IloNumArray vals(env); //declare an array to store the outputs
IloNumVarArray opvars(env); //if 2 dimensional: IloNumArray2 vals(env);
//env.out() << "Solution status = " << cplex.getStatus() << endl;
//return the status: Feasible/Optimal/Infeasible/Unbounded/Error/…
env.out() << "W_max value = " << cplex.getObjValue() << endl;
//return the optimal value for objective function
cplex.getValues(vals, var1); //get the variable outputs
//env.out() << "Values Var1 = " << vals << endl; //env.out() : output stream
cplex.getValues(vals, var3);
//env.out() << "Values Val3 = " << vals << endl;
}
catch (IloException& e) {
cerr << "Concert exception caught: " << e << endl;
}
catch (...) {
cerr << "Unknown exception caught" << endl;
}
env.end(); //close the CPLEX environment
float running_time (((float)end_time - (float)start_time)/CLOCKS_PER_SEC);
cout << "*******RUNNING TIME: " << running_time << endl;
return 0;
} // END main
int
main(int argc, char** argv)
{
IloEnv env;
try {
IloInt i, j;
const char* filename;
if (argc > 1)
filename = argv[1];
else
filename = "../../../examples/data/etsp.dat";
ifstream f(filename, ios::in);
if (!f) {
cerr << "No such file: " << filename << endl;
throw(1);
}
IntMatrix activityOnAResource(env);
NumMatrix duration(env);
IloNumArray jobDueDate(env);
IloNumArray jobEarlinessCost(env);
IloNumArray jobTardinessCost(env);
f >> activityOnAResource;
f >> duration;
f >> jobDueDate;
f >> jobEarlinessCost;
f >> jobTardinessCost;
IloInt nbJob = jobDueDate.getSize();
IloInt nbResource = activityOnAResource.getSize();
IloModel model(env);
// Create start variables
NumVarMatrix s(env, nbJob);
for (j = 0; j < nbJob; j++) {
s[j] = IloNumVarArray(env, nbResource, 0.0, Horizon);
}
// State precedence constraints
for (j = 0; j < nbJob; j++) {
for (i = 1; i < nbResource; i++) {
model.add(s[j][i] >= s[j][i-1] + duration[j][i-1]);
}
}
// State disjunctive constraints for each resource
for (i = 0; i < nbResource; i++) {
IloInt end = nbJob - 1;
for (j = 0; j < end; j++) {
IloInt a = activityOnAResource[i][j];
for (IloInt k = j + 1; k < nbJob; k++) {
IloInt b = activityOnAResource[i][k];
model.add(s[j][a] >= s[k][b] + duration[k][b] ||
s[k][b] >= s[j][a] + duration[j][a]);
}
}
}
// The cost is the sum of earliness or tardiness costs of each job
IloInt last = nbResource - 1;
IloExpr costSum(env);
for (j = 0; j < nbJob; j++) {
costSum += IloPiecewiseLinear(s[j][last] + duration[j][last],
IloNumArray(env, 1, jobDueDate[j]),
IloNumArray(env, 2, -jobEarlinessCost[j], jobTardinessCost[j]),
jobDueDate[j], 0);
}
model.add(IloMinimize(env, costSum));
costSum.end();
IloCplex cplex(env);
cplex.extract(model);
cplex.setParam(IloCplex::Param::Emphasis::MIP, 4);
if (cplex.solve()) {
cout << "Solution status: " << cplex.getStatus() << endl;
cout << " Optimal Value = " << cplex.getObjValue() << endl;
}
}
catch (IloException& ex) {
cerr << "Error: " << ex << endl;
}
catch (...) {
cerr << "Error" << endl;
}
env.end();
return 0;
}
// This function creates the following model:
// Minimize
// obj: x1 + x2 + x3 + x4 + x5 + x6
// Subject To
// c1: x1 + x2 + x5 = 8
// c2: x3 + x5 + x6 = 10
// q1: [ -x1^2 + x2^2 + x3^2 ] <= 0
// q2: [ -x4^2 + x5^2 ] <= 0
// Bounds
// x2 Free
// x3 Free
// x5 Free
// End
// which is a second order cone program in standard form.
// The function returns objective, variables and constraints in the
// values obj, vars and rngs.
// The function also sets up cone so that for a column j we have
// cone[j] >= 0 Column j is in a cone constraint and is the
// cone's head variable.
// cone[j] == NOT_CONE_HEAD Column j is in a cone constraint but is
// not the cone's head variable..
// cone[j] == NOT_IN_CONE Column j is not contained in any cone constraint.
static void
createmodel (IloModel& model, IloObjective &obj, IloNumVarArray &vars,
IloRangeArray &rngs, IloIntArray& cone)
{
// The indices we assign as user objects to the modeling objects.
// We define them as static data so that we don't have to worry about
// dynamic memory allocation/leakage.
static int indices[] = { 0, 1, 2, 3, 4, 5, 6 };
IloEnv env = model.getEnv();
// Create variables.
IloNumVar x1(env, 0, IloInfinity, "x1");
IloNumVar x2(env, -IloInfinity, IloInfinity, "x2");
IloNumVar x3(env, -IloInfinity, IloInfinity, "x3");
IloNumVar x4(env, 0, IloInfinity, "x4");
IloNumVar x5(env, -IloInfinity, IloInfinity, "x5");
IloNumVar x6(env, 0, IloInfinity, "x6");
// Create objective function and immediately store it in return value.
obj = IloMinimize(env, x1 + x2 + x3 + x4 + x5 + x6);
// Create constraints.
IloRange c1(env, 8, x1 + x2 + x5, 8, "c1");
IloRange c2(env, 10, x3 + x5 + x6, 10, "c2");
IloRange q1(env, -IloInfinity, -x1*x1 + x2*x2 + x3*x3, 0, "q1");
cone.add(2); // x1, cone head of constraint at index 2
cone.add(NOT_CONE_HEAD); // x2
cone.add(NOT_CONE_HEAD); // x3
IloRange q2(env, -IloInfinity, -x4*x4 + x5*x5, 0, "q2");
cone.add(3); // x4, cone head of constraint at index 3
cone.add(NOT_CONE_HEAD); // x5
cone.add(NOT_IN_CONE); // x6
// Setup model.
model.add(obj);
model.add(obj);
model.add(c1);
model.add(c2);
model.add(q1);
model.add(q2);
// Setup return values.
vars.add(x1);
vars.add(x2);
vars.add(x3);
vars.add(x4);
vars.add(x5);
vars.add(x6);
rngs.add(c1);
rngs.add(c2);
rngs.add(q1);
rngs.add(q2);
// We set the user object for each modeling object to its index in the
// respective array. This makes the code in checkkkt a little simpler.
for (IloInt i = 0; i < vars.getSize(); ++i)
vars[i].setObject(&indices[i]);
for (IloInt i = 0; i < rngs.getSize(); ++i)
rngs[i].setObject(&indices[i]);
}
Example(IloEnv env)
: nblocks(0), model(env), vars(env), ranges(env)
{
// Model data.
// fixed[] is the fixed cost for opening a facility,
// cost[i,j] is the cost for serving customer i from facility j.
static double const fixed[] = { 2.0, 3.0, 3.0 };
static double const cost[] = { 2.0, 3.0, 4.0, 5.0, 7.0,
4.0, 3.0, 1.0, 2.0, 6.0,
5.0, 4.0, 2.0, 1.0, 3.0 };
#define NFACTORY ((CPXDIM)(sizeof(fixed) / sizeof(fixed[0])))
#define NCUSTOMER ((CPXDIM)((sizeof(cost) / sizeof(cost[0])) / NFACTORY))
nblocks = NCUSTOMER;
IloExpr obj(env);
// Create integer y variables.
IloNumVarArray y(env);
for (IloInt f = 0; f < NFACTORY; ++f) {
std::stringstream s;
s << "y" << f;
IloIntVar v(env, 0, 1, s.str().c_str());
obj += fixed[f] * v;
objMap[v] = fixed[f];
y.add(v);
blockMap.insert(BlockMap::value_type(v, -1));
intersectMap.insert(IntersectMap::value_type(v, RowSet()));
}
// Create continuous x variables.
IloNumVarArray x(env);
for (IloInt f = 0; f < NFACTORY; ++f) {
for (IloInt c = 0; c < NCUSTOMER; ++c) {
std::stringstream s;
s << "x" << f << "#" << c;
IloNumVar v(env, 0.0, IloInfinity, s.str().c_str());
obj += v * cost[f * NCUSTOMER + c];
objMap[v] = cost[f * NCUSTOMER + c];
x.add(v);
blockMap.insert(BlockMap::value_type(v, c));
intersectMap.insert(IntersectMap::value_type(v, RowSet()));
}
}
vars.add(y);
vars.add(x);
model.add(vars);
// Add objective function.
model.add(IloMinimize(env, obj, "obj"));
objSense = IloObjective::Minimize;
obj.end();
// Satisfy each customer's demand.
for (IloInt c = 0; c < NCUSTOMER; ++c) {
std::stringstream s;
s << "c1_" << c;
IloRange r(env, 1.0, IloInfinity, s.str().c_str());
IloExpr lhs(env);
for (IloInt f = 0; f < NFACTORY; ++f) {
lhs += x[f * NCUSTOMER + c];
intersectMap[x[f * NCUSTOMER + c]].insert(r);
}
r.setExpr(lhs);
ranges.add(r);
lhs.end();
}
// A factory must be open if we service from it.
for (IloInt c = 0; c < NCUSTOMER; ++c) {
for (IloInt f = 0; f < NFACTORY; ++f) {
std::stringstream s;
s << "c2_" << c << "#" << f;
IloRange r(env, 0.0, IloInfinity, s.str().c_str());
intersectMap[x[f * NCUSTOMER + c]].insert(r);
intersectMap[y[f]].insert(r);
r.setExpr(-x[f * NCUSTOMER + c] + y[f]);
ranges.add(r);
}
}
// Capacity constraint.
IloRange r(env, -IloInfinity, NFACTORY - 1, "c3");
IloExpr lhs(env);
for (IloInt f = 0; f < NFACTORY; ++f) {
lhs += y[f];
intersectMap[y[f]].insert(r);
}
r.setExpr(lhs);
ranges.add(r);
lhs.end();
model.add(ranges);
#undef NFACTORY
#undef NCUSTOMER
}
int CProblem::setModel() {
//time_t start, end;
numvar = 1+n; // lambda + all c;
IloEnv env;
try {
IloModel model(env);
IloCplex cplex(env);
/*Variables*/
IloNumVar lambda(env, "lambda");
IloNumVarArray c(env, n);//
for (unsigned int u=0; u<n; u++) {
std::stringstream ss;
ss << u;
std::string str = "c" + ss.str();
c[u]=IloNumVar(env, str.c_str());
}
IloArray<IloIntVarArray> z(env,n);
for (unsigned int u=0; u<n; u++) {
z[u]= IloIntVarArray(env, n);
for (unsigned int v=0; v<n; v++) {
std::stringstream ss;
ss << u;
ss << v;
std::string str = "z" + ss.str();
z[u][v] = IloIntVar(env, 0, 1, str.c_str());
}
}
/* Constant M*/
int M=n*max_d;
UB = M;
/* Objective*/
model.add(IloMinimize(env, lambda));
//model.add(IloMinimize(env, IloSum(c)));
/*Constrains*/
model.add(lambda - UB <= 0);
/* d=function of the distance */
IloArray<IloNumArray> Par_d(env,n);
for (unsigned int u=0; u<n; u++) {
Par_d[u]=IloNumArray(env,n);
for (unsigned int v=0; v<n; v++) {
Par_d[u][v]=d[u][v];
}
}
for (unsigned u=0; u<n; u++) {
for (unsigned v=0; v<u; v++) {
model.add(c[v]-c[u]+M* z[u][v] >= Par_d[u][v] );
model.add(c[u]-c[v]+M*(1-z[u][v]) >= Par_d[u][v]);
numvar++; // + z[u][v]
}
}
/*
for (unsigned i=0; i<sqrt(n)-1; i=i+1) {
for (unsigned j=0; j<sqrt(n)-1; j=j+1) {
//square lattice
model.add (c[i*sqrt(n)+j] +c[i*sqrt(n)+j+1] +
c[(i+1)*sqrt(n)+j] +c[(i+1)*sqrt(n)+j+1]>= 16-4*sqrt(2)); // Bedingung fuer Quadratischen Gridgraph und d(x) = 3-x
//
// triangular lattice
// model.add (c[i*5+j]+c[i*5+j+1] +
// c[(i+1)+j] >= 4); // Bedingung fuer Quadratischen Gridgraph und d(x) = 3-x
// model.add (c[i*sqrt(n)+j]+c[i*sqrt(n)+j+1] +
// c[(i+1)*sqrt(n)+j]+c[(i+1)*sqrt(n)+j+1] >= 22 - 4*sqrt(2)); // Bedingung fuer Quadratischen Gridgraph und d(x) = 4-x
}
}
*/
/*
for (unsigned i=0; i<sqrt(n)-2; i+=3) {
for (unsigned j=0; j<sqrt(n)-2; j+=3) {
// model.add (c[i*sqrt(n)+j] + c[i*sqrt(n)+j+1] + c[i*sqrt(n)+j+2] +
// c[(i+1)*sqrt(n)+j]+ c[(i+1)*sqrt(n)+j+1]+ c[(i+1)*sqrt(n)+j+2] +
// c[(i+2)*sqrt(n)+j]+ c[(i+2)*sqrt(n)+j+1]+ c[(i+2)*sqrt(n)+j+2]
// >= 60-17*sqrt(2)-3*sqrt(5)); // Bedingung fuer Quadratischen Gridgraph und d(x) = 3-x
// model.add (c[i*sqrt(n)+j] + c[i*sqrt(n)+j+1] + c[i*sqrt(n)+j+2] +
// c[(i+1)*sqrt(n)+j]+ c[(i+1)*sqrt(n)+j+1]+ c[(i+1)*sqrt(n)+j+2] +
// c[(i+2)*sqrt(n)+j]+ c[(i+2)*sqrt(n)+j+1]+ c[(i+2)*sqrt(n)+j+2]
// >= 82-8*sqrt(2)-2*sqrt(5)); // Bedingung fuer Quadratischen Gridgraph und d(x) = 4-x
}
}
*/
for (unsigned int v=0; v<n; v++) {
IloExpr expr;
model.add (c[v] <= lambda);
model.add (c[v] >= 0);
expr.end();
}
std::cout << "Number of variables " << numvar << "\n";
/* solve the Model*/
cplex.extract(model);
cplex.exportModel("L-Labeling.lp");
/*
start = clock();
int solveError = cplex.solve();
end = clock ();
*/
IloTimer timer(env);
timer.start();
int solveError = cplex.solve();
timer.stop();
if ( !solveError ) {
std::cout << "STATUS : "<< cplex.getStatus() << "\n";
env.error() << "Failed to optimize LP \n";
exit(1);
}
//Info.time = (double)(end-start)/(double)CLOCKS_PER_SEC;
Info.time = timer.getTime();
std::cout << "STATUS : "<< cplex.getStatus() << "\n";
/* get the solution*/
env.out() << "Solution status = " << cplex.getStatus() << "\n";
numconst = cplex.getNrows();
env.out() << " Number of constraints = " << numconst << "\n";
lambda_G_d=cplex.getObjValue();
env.out() << "Solution value = " << lambda_G_d << "\n";
for (unsigned int u=0; u<n; u++) {
C.push_back(cplex.getValue(c[u]));
std::cout << "c(" << u << ")=" << C[u]<< " ";
}
std::cout <<"\n";
/*
for (unsigned int u=0; u<n; u++) {
for (unsigned int v=0; v<u; v++) {
std::cout<< "z[" << u <<"][" << v << "]="<< cplex.getValue( z[u][v]) << " ";
Z.push_back(cplex.getValue( z[u][v]));
}
std::cout << "\n";
}
std::cout <<"\n";
*/
} // end try
catch (IloException& e) {
std::cerr << "Concert exception caught: " << e << std::endl;
}
catch (...) {
std::cerr << "Unknown exception caught" << std::endl;
}
env.end();
return 0;
}
ILOSTLBEGIN
int
main(int, char**)
{
IloEnv env;
try {
IloInt nbMachines = 6;
IloNumArray cost (env, nbMachines,
15.0, 20.0, 45.0, 64.0, 12.0, 56.0);
IloNumArray capacity (env, nbMachines,
100.0, 20.0, 405.0, 264.0, 12.0, 256.0);
IloNumArray fixedCost(env, nbMachines,
1900.0, 820.0, 805.0, 464.0, 3912.00, 556.0);
IloNum demand = 22.0;
IloModel model(env);
IloNumVarArray x(env, nbMachines, 0, IloInfinity);
IloNumVarArray fused(env, nbMachines, 0, 1, ILOINT);
// Objective: minimize the sum of fixed and variable costs
model.add(IloMinimize(env, IloScalProd(cost, x)
+ IloScalProd(fused, fixedCost)));
IloInt i;
for(i = 0; i < nbMachines; i++) {
// Constraint: respect capacity constraint on machine 'i'
model.add(x[i] <= capacity[i]);
// Constraint: only produce product on machine 'i' if it is 'used'
// (to capture fixed cost of using machine 'i')
model.add(x[i] <= capacity[i]*fused[i]);
}
// Constraint: meet demand
model.add(IloSum(x) == demand);
IloCplex cplex(env);
cplex.extract(model);
cplex.solve();
cout << "Solution status: " << cplex.getStatus() << endl;
cout << "Obj " << cplex.getObjValue() << endl;
IloNum eps = cplex.getParam(
IloCplex::Param::MIP::Tolerances::Integrality);
for(i = 0; i < nbMachines; i++) {
if (cplex.getValue(fused[i]) > eps) {
cout << "E" << i << " is used for ";
cout << cplex.getValue(x[i]) << endl;
}
}
cout << endl;
cout << "----------------------------------------" << endl;
}
catch (IloException& ex) {
cerr << "Error: " << ex << endl;
}
env.end();
return 0;
}
