/// Actual model
TSP(const SizeOptions& opt)
: p(ps[opt.size()]),
succ(*this, p.size(), 0, p.size()-1),
total(*this, 0, p.max()) {
int n = p.size();
// Cost matrix
IntArgs c(n*n, p.d());
for (int i=n; i--; )
for (int j=n; j--; )
if (p.d(i,j) == 0)
rel(*this, succ[i], IRT_NQ, j);
// Cost of each edge
IntVarArgs costs(*this, n, Int::Limits::min, Int::Limits::max);
// Enforce that the succesors yield a tour with appropriate costs
circuit(*this, c, succ, costs, total, opt.icl());
// Just assume that the circle starts forwards
{
IntVar p0(*this, 0, n-1);
element(*this, succ, p0, 0);
rel(*this, p0, IRT_LE, succ[0]);
}
// First enumerate cost values, prefer those that maximize cost reduction
branch(*this, costs, INT_VAR_REGRET_MAX_MAX(), INT_VAL_SPLIT_MIN());
// Then fix the remaining successors
branch(*this, succ, INT_VAR_MIN_MIN(), INT_VAL_MIN());
}
int chooseVar6(const Problem<type,type2>& P,type epsilon){
type2 max=std::numeric_limits<type2>::min();
int j=-1;
int i=0;
while (i<P.nbTask){
if (P.A[i].Fi(P.bmax(i))*(P.d(i)-P.r(i))-P.W(i)>=max && P.smax(i) - P.r(i)>epsilon) {
j=i;
max=P.A[i].Fi(P.bmax(i))*(P.d(i)-P.r(i))-P.W(i);
}
if (P.A[i].Fi(P.bmax(i))*(P.d(i)-P.r(i))-P.W(i)>max && P.d(i)-P.emin(i)>epsilon){
j=i+P.nbTask;
max=P.A[i].Fi(P.bmax(i))*(P.d(i)-P.r(i))-P.W(i);
}
++i;
}
return j;
}
int chooseVar3(const Problem<type,type2>& P,type epsilon){
type min=std::numeric_limits<type>::max();
int j=-1;
int i=0;
while (i<P.nbTask){
if (P.smax(i) - P.r(i)<=min && P.smax(i) - P.r(i)>epsilon) {
j=i;
min=P.smax(i) - P.r(i);
}
if (P.d(i) - P.emin(i) < min && P.d(i) - P.emin(i)>epsilon){
j=i+P.nbTask;
min=P.d(i) - P.emin(i);
}
++i;
}
return j;
}
int chooseVar1(const Problem<type,type2>& P, type epsilon){
int i=0;
while (i<P.nbTask){
if (P.smax(i) - P.r(i) > epsilon) return i;
if (P.d(i) - P.emin(i) > epsilon) return i+P.nbTask;
++i;
}
return -1;
}
int createVars(const Problem<double>& P, IloEnv& env, IloNumVarMatrix& x, IloNumVarMatrix& y, IloNumVarMatrix& b, IloNumVarMatrix& w){
try{
int i;
const int nbTask=P.nbTask;
const int E= 2*nbTask;
for (i=0;i<nbTask;i++){
x[i]=IloNumVarArray(env, E, 0, 1, ILOINT);
y[i]=IloNumVarArray(env, E, 0, 1, ILOINT);
b[i]=IloNumVarArray(env, E-1, 0, ((P.d(i)-P.r(i))*P.bmax(i)), ILOFLOAT);
w[i]=IloNumVarArray(env, E-1, 0, (P.d(i)-P.r(i))*P.A[i].Fi(P.bmax(i)), ILOFLOAT);
}
return 0;
}
catch (IloException &e){
std::cout << "iloexception in create vars" <<e <<std::endl;
e.end();
return 1;
}
}
int chooseVar2(const Problem<type,type2>& P,type epsilon){
const uint n = P.nbTask;
int real_size = 0;
uint i;
int s, j = -1;
for (i = 0 ; i < n ; ++i) {
if (P.smax(i) - P.r(i) > epsilon) ++real_size;
if (P.d(i) - P.emin(i) > epsilon) ++real_size;
}
if (!real_size) return -1;
if (real_size == 1) s = 1;
else s = rand() % real_size;
for (i = 0 ; i < n && j < 0 ; ++i) {
if (P.smax(i) - P.r(i) > epsilon) --s;
if (!s) j = i;
else {
if (P.d(i) - P.emin(i)> epsilon) --s;
if (!s) j = i +n;
}
}
return j;
}
int createConstraintTimeW(const Problem<double>& P, IloModel& model,
IloNumVarArray& t, IloNumVarMatrix& x, IloNumVarMatrix& y, const std::vector<double>& M_te){
const int E=2*P.nbTask;
for (int i=0;i<P.nbTask;i++) {
for (int e=0;e<E;++e){
model.add(x[i][e]*P.r(i) <= t[e]);
model.add(t[e] <= x[i][e]*P.smax(i) +
(1-x[i][e])*M_te[e]);
model.add(P.d(i)*y[i][e] + (1-y[i][e])*M_te[e]>= t[e]);
model.add(t[e] >= y[i][e]*P.emin(i));
}
}
return 0;
}
void createBranch(Problem<type,type2>& P,int x,std::stack<Problem<type,type2>>& explore, double param){
Problem<type,type2> Q(P);
if (x < P.nbTask){
P.A[x].smax-=(P.smax(x)-P.r(x))*(1.0-param);
explore.push(P);
Q.A[x].ri+=(Q.smax(x)-Q.r(x))*param;
Q.A[x].updateEMin();
explore.push(Q);
}
else {
P.A[x-P.nbTask].emin+=(P.d(x-P.nbTask)-P.emin(x-P.nbTask))*(1.0-param);
explore.push(P);
Q.A[x-Q.nbTask].di-=(Q.d(x-Q.nbTask)-Q.emin(x-Q.nbTask))*param;
Q.A[x-Q.nbTask].updateSMax();
explore.push(Q);
}
}
int createConstraintNonConsump(const Problem<double>& P, IloModel& model, IloEnv& env,
IloNumVarMatrix& x, IloNumVarMatrix& y , IloNumVarMatrix& b, int bigM){
for (int i=0;i<P.nbTask;i++){
for (int e=0;e<2*P.nbTask-1;e++){
IloExpr expr(env);
for (int f=0;f<=e;f++){
expr+= x[i][f];
expr-= y[i][f];
}
if (!bigM)
expr*=P.bmax(i)*(P.d(i)-P.r(i));
else
expr*=M;
model.add(expr >=b[i][e]);
expr.end();
}
}
return 0;
}
int createConstraintBmin(const Problem<double>&P, IloModel& model, IloEnv& env, IloNumVarArray& t, IloNumVarMatrix& x, IloNumVarMatrix& y,IloNumVarMatrix& b, const int& bigM){
for (int i=0;i<P.nbTask;i++){
for (int e=0;e<2*P.nbTask-1;e++){
IloExpr expr(env);
for (int f=0;f<=e;f++){
expr-= x[i][f];
expr+= y[i][f];
}
expr+=1;
if (bigM)
expr*=-M;
else
expr*= - P.bmin(i)*(P.d(i)-P.r(i));
expr+=(t[e+1]-t[e])*P.A[i].bmin;
model.add(expr <= b[i][e]);
expr.end();
}
}
return 0;
}
