ILOCPLEXGOAL1(MyBranchGoal, IloNumVarArray, vars) {
IloNumArray x;
IloNumArray obj;
IntegerFeasibilityArray feas;
x = IloNumArray(getEnv());
obj = IloNumArray(getEnv());
feas = IntegerFeasibilityArray(getEnv());
getValues(x, vars);
getObjCoefs(obj, vars);
getFeasibilities(feas, vars);
IloInt bestj = -1;
IloNum maxinf = 0.0;
IloNum maxobj = 0.0;
IloInt cols = vars.getSize();
for (IloInt j = 0; j < cols; j++) {
if ( feas[j] == Infeasible ) {
IloNum xj_inf = x[j] - IloFloor (x[j]);
if ( xj_inf > 0.5 )
xj_inf = 1.0 - xj_inf;
if ( xj_inf >= maxinf &&
(xj_inf > maxinf || IloAbs (obj[j]) >= maxobj) ) {
bestj = j;
maxinf = xj_inf;
maxobj = IloAbs (obj[j]);
}
}
}
IloCplex::Goal res;
if ( bestj >= 0 ) {
res = AndGoal(OrGoal(vars[bestj] >= IloFloor(x[bestj])+1,
vars[bestj] <= IloFloor(x[bestj])),
this);
}
x.end();
obj.end();
feas.end();
return res;
}
ILOBRANCHCALLBACK1(MyBranch, IloNumVarArray, vars) {
if ( getBranchType() != BranchOnVariable )
return;
// Branch on var with largest objective coefficient
// among those with largest infeasibility
IloNumArray x;
IloNumArray obj;
IntegerFeasibilityArray feas;
try {
x = IloNumArray(getEnv());
obj = IloNumArray(getEnv());
feas = IntegerFeasibilityArray(getEnv());
getValues(x, vars);
getObjCoefs(obj, vars);
getFeasibilities(feas, vars);
IloInt bestj = -1;
IloNum maxinf = 0.0;
IloNum maxobj = 0.0;
IloInt cols = vars.getSize();
for (IloInt j = 0; j < cols; j++) {
if ( feas[j] == Infeasible ) {
IloNum xj_inf = x[j] - IloFloor (x[j]);
if ( xj_inf > 0.5 )
xj_inf = 1.0 - xj_inf;
if ( xj_inf >= maxinf &&
(xj_inf > maxinf || IloAbs (obj[j]) >= maxobj) ) {
bestj = j;
maxinf = xj_inf;
maxobj = IloAbs (obj[j]);
}
}
}
if ( bestj >= 0 ) {
makeBranch(vars[bestj], x[bestj], IloCplex::BranchUp, getObjValue());
makeBranch(vars[bestj], x[bestj], IloCplex::BranchDown, getObjValue());
}
}
catch (...) {
x.end();
obj.end();
feas.end();
throw;
}
x.end();
obj.end();
feas.end();
}
int main(int argc, const char* argv[]){
IloEnv env;
try {
const char* filename = "../../../examples/data/learningeffect_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();
}
IloModel model(env);
IloInt nbJobs, nbMachines;
file >> nbJobs;
file >> nbMachines;
IloIntervalVarArray2 machines(env, nbMachines);
IloIntArray2 sizes(env, nbMachines);
for (IloInt j = 0; j < nbMachines; j++) {
machines[j] = IloIntervalVarArray(env);
sizes[j] = IloIntArray(env);
}
IloIntExprArray ends(env);
for (IloInt i = 0; i < nbJobs; i++) {
IloIntervalVar prec;
for (IloInt j = 0; j < nbMachines; j++) {
IloInt m, d;
file >> m;
file >> d;
IloIntervalVar ti(env);
machines[m].add(ti);
sizes[m].add(d);
if (0 != prec.getImpl())
model.add(IloEndBeforeStart(env, prec, ti));
prec = ti;
}
ends.add(IloEndOf(prec));
}
for (IloInt j = 0; j < nbMachines; j++) {
IloInt lef;
file >> lef;
IloNum alpha = lef/100.0;
IloIntervalVarArray chain(env);
IloIntVarArray indices(env);
IloIntervalVar prec;
for(IloInt i = 0; i < nbJobs; ++i) {
IloIntervalVar ti = machines[j][i];
IloInt d = sizes[j][i];
ti.setSizeMax(d);
// Building of the chain of intervals for the machine.
IloIntervalVar syncti(env);
if (prec.getImpl())
model.add(IloEndBeforeStart(env, prec, syncti));
prec = syncti;
IloIntVar index(env, 0, nbJobs - 1);
// Learning effect captured by the decreasing function
// of the position (0 <= alpha <= 1).
// At first position in the sequence index = 0; there is no
// learning effect and duration of the task is its nominal duration
model.add(IloSizeOf(ti) == IloFloor(IloNum(d)*IloPower(alpha, index)));
chain.add(syncti);
indices.add(index);
}
model.add(IloIsomorphism(env, chain, machines[j], indices, nbJobs));
// The no-overlap is a redundant constraint in this quite simple
// model - it is used only to provide stronger inference.
model.add(IloNoOverlap(env, machines[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;
}
