void profit_create_vars(graph g, IloModel model, IloNumVarArray x, IloNumVarArray p, IloNumVarArray z, int **columns) {
IloEnv env = model.getEnv();
string s;
assignment_vars(g, model, x, columns);
for(int j = 0; j < g->items; j++) {
s = "p_" + itos(j);
p.add(IloNumVar(env, 0.0, boundp(j), ILOFLOAT, s.c_str()));
// p.add(IloNumVar(env, lowerp(j), boundp(j), ILOFLOAT, s.c_str()));
}
for(int i = 0; i < g->bidders; i++) {
s = "z_" + itos(i);
z.add(IloNumVar(env, 0.0, boundu(i), ILOFLOAT, s.c_str()));
}
}
void addInitColumn(IloNumVarArray lambda, IloObjective rmpObj,
IloRangeArray maintConEng, IloRangeArray removeMod, IloRangeArray convex,
IloNumArray2 addXCol, IloNumArray addZCol,
const IloNumArray compCosts, const IloNumArray convexityCoef) {
// loop counter
IloInt t;
// counter for objective function coefficient for lambda
// variable to be added.
IloNum lambdaObjCoef = 0;
// function assumes addXCol and addZCol contains proper values.
// calculate objective function coefficient lambdaObjCoef
for (t = 0; t < TIME_SPAN; t++) {
// for each fixed t: scalar product of x[m]* vector and
// component costs vector compCosts[m]:
lambdaObjCoef += IloScalProd(addXCol[t],compCosts);
// also clear the addXCol subarrays as soon as they
// have been used
addXCol[t].clear();
}
// now add this column and it's associated lambda variable to the RMP.
lambda.add(IloNumVar(rmpObj(lambdaObjCoef) + maintConEng(addZCol) +
removeMod(addZCol) + convex(convexityCoef), 0.0, 1.0));
// clear addZCol num array.
addZCol.clear();
} // END of addColumn
static void
populatebycolumn (IloModel model, IloNumVarArray x, IloRangeArray c)
{
IloEnv env = model.getEnv();
IloObjective obj = IloMaximize(env);
c.add(IloRange(env, -IloInfinity, 20.0));
c.add(IloRange(env, -IloInfinity, 30.0));
x.add(IloNumVar(obj(1.0) + c[0](-1.0) + c[1]( 1.0), 0.0, 40.0));
x.add(obj(2.0) + c[0]( 1.0) + c[1](-3.0));
x.add(obj(3.0) + c[0]( 1.0) + c[1]( 1.0));
model.add(obj);
model.add(c);
} // END populatebycolumn
void addColumn(IloCplex subSolver, IloNumVarArray2 x, IloNumVarArray z, IloNumVarArray lambda,
IloObjective rmpObj, IloRangeArray maintConEng, IloRangeArray removeMod,
IloRangeArray convex, IloNumArray2 addXCol, IloNumArray addZCol,
const IloNumArray compCosts, const IloNumArray convexityCoef) {
// loop counter
IloInt t;
// counter for objective function coefficient for lambda
// variable to be added.
IloNum lambdaObjCoef = 0;
// extract subproblem-optimal solution values
// (into IloNumArrays addXCol (2d) and addZCol (1d)).
// z values:
subSolver.getValues(addZCol,z);
//cout << endl << endl << "z = " << endl << addZCol << endl;
//cin.get();
// !!! OBS !!!
// here we might want to save these z values some column pool's custom-nitted class
// array. Or to be specific, we want to add the indexes for NON-ZERO-ENTRIES in addZCol
// to our class that keep place of columns.
// E.g., given variable lambda(m)_(q_m), we want to know in our own class object,
// given (m)(q_m), the indexes of non-zeros in that Z column.
// and for each t...
for (t = 0; t < TIME_SPAN; t++) {
// x values:
subSolver.getValues(addXCol[t],x[t]);
//cout << endl << endl << "x[t=" << t << "] =" << endl << addXCol[t] << endl;
}
//cin.get();
// calculate objective function coefficient lambdaObjCoef
for (t = 0; t < TIME_SPAN; t++) {
// for each fixed t: scalar product of x[m]* vector and
// component costs vector compCosts[m]:
lambdaObjCoef += IloScalProd(addXCol[t],compCosts);
// also clear the addXCol subarrays as soon as they
// have been used
addXCol[t].clear();
}
// now add this column and it's associated lambda variable to the RMP.
lambda.add(IloNumVar(rmpObj(lambdaObjCoef) + maintConEng(addZCol) +
removeMod(addZCol) + convex(convexityCoef), 0.0, 1.0));
// clear addZCol num array.
addZCol.clear();
} // END of addColumn
static void
populatebyrow (IloModel model, IloNumVarArray x, IloRangeArray c)
{
IloEnv env = model.getEnv();
x.add(IloNumVar(env, 0.0, 40.0));
x.add(IloNumVar(env));
x.add(IloNumVar(env));
x.add(IloNumVar(env, 2.0, 3.0, ILOINT));
model.add(IloMaximize(env, x[0] + 2 * x[1] + 3 * x[2] + x[3]));
c.add( - x[0] + x[1] + x[2] + 10 * x[3] <= 20);
c.add( x[0] - 3 * x[1] + x[2] <= 30);
c.add( x[1] - 3.5* x[3] == 0);
model.add(c);
} // END populatebyrow
static void
populatebyrow (IloModel model, IloNumVarArray x, IloRangeArray c)
{
IloEnv env = model.getEnv();
x.add(IloNumVar(env, 0.0, 40.0));
x.add(IloNumVar(env));
x.add(IloNumVar(env));
model.add(IloMaximize(env, x[0] + 2 * x[1] + 3 * x[2]
- 0.5 * (33*x[0]*x[0] + 22*x[1]*x[1] +
11*x[2]*x[2] - 12*x[0]*x[1] -
23*x[1]*x[2] ) ));
c.add( - x[0] + x[1] + x[2] <= 20);
c.add( x[0] - 3 * x[1] + x[2] <= 30);
model.add(c);
} // END populatebyrow
void PopulateFromGraph(IloModel model, IloNumVarArray s, IloRangeArray c){
IloEnv env = model.getEnv();
// Used n+1 for accomodating an extra variable
// For being able to write the objective function
for(int i = 0; i <= n; i++)
s.add(IloNumVar(env));
}
void generateProblem(const ILPModel& m, IloModel& model, IloNumVarArray& x, IloRangeArray& con) {
IloEnv env = model.getEnv();
IloObjective obj = (m.obj == MINIMIZE ? IloMinimize(env) : IloMaximize(env));
for (unsigned long v = 0; v < m.numberOfVariables(); ++v) {
switch (m.x[v].type) {
case FLT:
x.add(IloNumVar(env, m.x[v].lowerBound, m.x[v].upperBound, IloNumVar::Float));
break;
case BIN:
x.add(IloNumVar(env, m.x[v].lowerBound, m.x[v].upperBound, IloNumVar::Bool));
break;
default:
x.add(IloNumVar(env, m.x[v].lowerBound, m.x[v].upperBound, IloNumVar::Int));
}
obj.setLinearCoef(x[v], m.c[v]);
x[v].setName(m.varDesc[v].c_str());
}
for (unsigned long c = 0; c < m.numberOfConstraints(); ++c) {
switch (m.ops[c]) {
case LESS_EQUAL:
con.add(IloRange(env, -IloInfinity, m.b[c]));
break;
case EQUAL:
con.add(IloRange(env, m.b[c], m.b[c]));
break;
case GREATER_EQUAL:
con.add(IloRange(env, m.b[c], IloInfinity));
}
for (const pair<uint32_t, double>& p : m.A[c])
con[c].setLinearCoef(x[p.first], p.second);
con[c].setName(m.conDesc[c].c_str());
}
model.add(obj);
model.add(con);
}
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
static void
populatebyrow (IloModel model, IloNumVarArray x, IloRangeArray c)
{
IloEnv env = model.getEnv();
x.add(IloNumVar(env, 0.0, 40.0));
x.add(IloNumVar(env, 0.0, IloInfinity, ILOINT));
x.add(IloNumVar(env, 0.0, IloInfinity, ILOINT));
x.add(IloNumVar(env, 2.0, 3.0, ILOINT));
model.add(IloMaximize(env, x[0] + 2 * x[1] + 3 * x[2] + x[3]));
c.add( - x[0] + x[1] + x[2] + 10 * x[3] <= 20);
c.add( x[0] - 3 * x[1] + x[2] <= 30);
c.add( x[1] - 3.5* x[3] == 0);
model.add(c);
IloNumVarArray sosvar(env, 2);
IloNumArray sosval(env, 2);
sosvar[0] = x[2]; sosvar[1] = x[3];
sosval[0] = 25.0; sosval[1] = 18.0;
model.add(IloSOS1(model.getEnv(), sosvar, sosval));
} // END populatebyrow
// 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]);
}
// This routine set up the IloCplex algorithm to solve the worker LP, and
// creates the worker LP (i.e., the dual of flow constraints and
// capacity constraints of the flow MILP)
//
// Modeling variables:
// forall k in V0, i in V:
// u(k,i) = dual variable associated with flow constraint (k,i)
//
// forall k in V0, forall (i,j) in A:
// v(k,i,j) = dual variable associated with capacity constraint (k,i,j)
//
// Objective:
// minimize sum(k in V0) sum((i,j) in A) x(i,j) * v(k,i,j)
// - sum(k in V0) u(k,0) + sum(k in V0) u(k,k)
//
// Constraints:
// forall k in V0, forall (i,j) in A: u(k,i) - u(k,j) <= v(k,i,j)
//
// Nonnegativity on variables v(k,i,j)
// forall k in V0, forall (i,j) in A: v(k,i,j) >= 0
//
void
createWorkerLP(IloCplex cplex, IloNumVarArray v, IloNumVarArray u,
IloObjective obj, IloInt numNodes)
{
IloInt i, j, k;
IloEnv env = cplex.getEnv();
IloModel mod(env, "atsp_worker");
// Set up IloCplex algorithm to solve the worker LP
cplex.extract(mod);
cplex.setOut(env.getNullStream());
// Turn off the presolve reductions and set the CPLEX optimizer
// to solve the worker LP with primal simplex method.
cplex.setParam(IloCplex::Reduce, 0);
cplex.setParam(IloCplex::RootAlg, IloCplex::Primal);
// Create variables v(k,i,j) forall k in V0, (i,j) in A
// For simplicity, also dummy variables v(k,i,i) are created.
// Those variables are fixed to 0 and do not partecipate to
// the constraints.
IloInt numArcs = numNodes * numNodes;
IloInt vNumVars = (numNodes-1) * numArcs;
IloNumVarArray vTemp(env, vNumVars, 0, IloInfinity);
for (k = 1; k < numNodes; ++k) {
for (i = 0; i < numNodes; ++i) {
vTemp[(k-1)*numArcs + i *numNodes + i].setBounds(0, 0);
}
}
v.clear();
v.add(vTemp);
vTemp.end();
mod.add(v);
// Set names for variables v(k,i,j)
for (k = 1; k < numNodes; ++k) {
for(i = 0; i < numNodes; ++i) {
for(j = 0; j < numNodes; ++j) {
char varName[100];
sprintf(varName, "v.%d.%d.%d", (int) k, (int) i, (int) j);
v[(k-1)*numArcs + i*numNodes + j].setName(varName);
}
}
}
// Associate indices to variables v(k,i,j)
IloIntArray vIndex(env, vNumVars);
for (j = 0; j < vNumVars; ++j)
{
vIndex[j] = j;
v[j].setObject(&vIndex[j]);
}
// Create variables u(k,i) forall k in V0, i in V
IloInt uNumVars = (numNodes-1) * numNodes;
IloNumVarArray uTemp(env, uNumVars, -IloInfinity, IloInfinity);
u.clear();
u.add(uTemp);
uTemp.end();
mod.add(u);
// Set names for variables u(k,i)
for (k = 1; k < numNodes; ++k) {
for(i = 0; i < numNodes; ++i) {
char varName[100];
sprintf(varName, "u.%d.%d", (int) k, (int) i);
u[(k-1)*numNodes + i].setName(varName);
}
}
// Associate indices to variables u(k,i)
IloIntArray uIndex(env, uNumVars);
for (j = 0; j < uNumVars; ++j)
{
uIndex[j] = vNumVars + j;
u[j].setObject(&uIndex[j]);
}
// Initial objective function is empty
obj.setSense(IloObjective::Minimize);
mod.add(obj);
// Add constraints:
// forall k in V0, forall (i,j) in A: u(k,i) - u(k,j) <= v(k,i,j)
for (k = 1; k < numNodes; ++k) {
for(i = 0; i < numNodes; ++i) {
for(j = 0; j < numNodes; ++j) {
if ( i != j ) {
IloExpr expr(env);
expr -= v[(k-1)*numArcs + i*(numNodes) + j];
expr += u[(k-1)*numNodes + i];
expr -= u[(k-1)*numNodes + j];
mod.add(expr <= 0);
expr.end();
}
}
}
}
}// END createWorkerLP
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
}
void LpSolver::populatebyrow (CplexConverter& cplexConverter,
IloModel model, IloNumVarArray x, IloRangeArray c)
{
IloEnv env = model.getEnv();
// CAPITAL LETTERS MEAN I NEED YOUR HELP, here is help
// IloExpr cost(env);
// Create Variables
// cout << "size of var: " << cplexConverter.variables.size() << endl;
for (int i = 0; i < cplexConverter.variables.size(); ++i){
IloNumVar iloVar(env, 0.0, cplexConverter.capacities[i], IloNumVar::Int);
// cout << iloVar << endl;
x.add(iloVar);
}
//Capacity Constraints
for (auto &it : cplexConverter.atomicIdToVarIdDict){
IloExpr t(env);
// cout << "adding constraint ";
for (int j = 0; j < it.second.size(); j++){
// cout << "x[" << it.second[j] << "] + ";
t += x[it.second[j]];
}
// cout << endl;
c.add(t <= cplexConverter.graph->atomicEdges[it.first]->capacity);
// cout << c << endl;
t.end();
}
// other constraints
for (auto nodePair : cplexConverter.graph->nodes){
// For all nodes
Node* n = nodePair.second;
if(n == cplexConverter.src){
// source constraints
// IloExpr inFlow(env);
IloExpr outFlow(env);
for(auto &atoIn : n->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];
outFlow += x[vId];
// cost += cplexConverter.graph->atomicEdges[cplexConverter.variables[vId].atomicEdgeId]->interest_rate * x[vId];
}
}
for (auto &atoOut : n->atomicEdge_out){
int aeId = atoOut.second->atomicEdgeId;
for (int j = 0; j < cplexConverter.atomicIdToVarIdDict[aeId].size(); j++){
// var Id
int vId = cplexConverter.atomicIdToVarIdDict[aeId][j];
// inFlow += x[vId];
c.add(x[vId] == 0);
// cost -= cplexConverter.graph->atomicEdges[cplexConverter.variables[vId].atomicEdgeId]->interest_rate * x[vId];
}
}
c.add(outFlow == cplexConverter.request);
// inFlow.end();
outFlow.end();
} else if(n == cplexConverter.dest){
// destination constraints
IloExpr inFlow(env);
// IloExpr outFlow(env);
for(auto &atoIn : n->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];
// outFlow += x[vId];
c.add(x[vId] == 0);
}
}
for (auto &atoOut : n->atomicEdge_out){
int aeId = atoOut.second->atomicEdgeId;
for (int j = 0; j < cplexConverter.atomicIdToVarIdDict[aeId].size(); j++){
// var Id
int vId = cplexConverter.atomicIdToVarIdDict[aeId][j];
inFlow += x[vId];
}
}
c.add(inFlow == cplexConverter.request);
inFlow.end();
// outFlow.end();
} else {
// Monotonicity Constraints
for (int i = 0; i < credNetConstants.totalIrs.size(); ++i){
IloExpr tempin(env);
IloExpr tempout(env);
for (auto &atoIn : n->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];
if (cplexConverter.variables[vId].interest_rate <= credNetConstants.totalIrs[i]){
tempout += x[vId];
}
}
}
for (auto &atoOut : n->atomicEdge_out){
int aeId = atoOut.second->atomicEdgeId;
for (int j = 0; j < cplexConverter.atomicIdToVarIdDict[aeId].size(); j++){
// var Id
int vId = cplexConverter.atomicIdToVarIdDict[aeId][j];
if (cplexConverter.variables[vId].interest_rate <= credNetConstants.totalIrs[i]){
tempin += x[vId];
}
}
}
c.add(tempout - tempin >= 0);
tempout.end();
tempin.end();
}
//Flow Constraints
IloExpr inFlow(env);
IloExpr outFlow(env);
for(auto &atoIn : n->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];
outFlow += x[vId];
}
}
for (auto &atoOut : n->atomicEdge_out){
int aeId = atoOut.second->atomicEdgeId;
for (int j = 0; j < cplexConverter.atomicIdToVarIdDict[aeId].size(); j++){
// var Id
int vId = cplexConverter.atomicIdToVarIdDict[aeId][j];
inFlow += x[vId];
}
}
c.add(inFlow - outFlow == 0);
inFlow.end();
outFlow.end();
}
}
model.add(c);
// model.add(IloMinimize(env, cost));
// model.add(IloMaximize(env,cost)); //option to minimize cost
// cost.end();
} // END populatebyrow
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);
}