void BVH_balancing::Solve( const std::map<int, std::set<int> > &compatibility, size_t no_bones,
std::map<int, int>& assignments ){
std::map<int, int> Bn_to_index, index_to_Bn;
size_t index = 0;
size_t no_branchings = compatibility.size();
assert( no_bones > 0 );
assert( no_branchings > 0 );
for( const auto& item : compatibility ){
if( Bn_to_index.count(item.first) > 0 ) { continue; } // skip if alredy mapped
Bn_to_index[item.first] = index;
index_to_Bn[index] = item.first;
++index;
}
// entire optimization goes inside a try catch statement.
// in case of errors, I want it to crash!
try{
GRBEnv env;
GRBModel model = GRBModel( env );
/* CREATE VARIABLES */
std::vector<GRBVar> vars( no_bones * no_branchings );
for( size_t r = 0; r < no_bones; ++r ){
for( size_t c = 0; c < no_branchings; ++c ){
size_t idx = matIndex( r, c, no_branchings );
std::stringstream var_name;
var_name << "A( " << r << ", " << c << " )";
assert( compatibility.count( index_to_Bn[c] ) > 0 );
bool is_connected = compatibility.at( index_to_Bn[c] ).count( r );
vars[idx] = model.addVar( 0.0, ( is_connected > 0 ? 1.0 : 0.0 ), 1.0, GRB_BINARY, var_name.str());
}
}
model.update();
// Create Objective Function.
// for each branching node i : #bones( Bn_i )^2
// hence I need to sum the number of bones assigned to each branching node
GRBQuadExpr obj;
std::vector<GRBLinExpr> cols( no_branchings );
for( size_t c = 0; c < no_branchings; ++c ){
for( size_t r = 0; r < no_bones; ++r ){
size_t idx = matIndex( r, c, no_branchings );
cols[c] += vars[idx];
}
}
for( size_t c = 0; c < no_branchings; ++c ){ obj += cols[c] * cols[c]; }
model.setObjective( obj );
model.update();
// create constraint : each bone can be assigned to only one branching node.
// this means that the summation of each row's values must be equal to 1.0
for( size_t r = 0; r < no_bones; ++r ){
GRBLinExpr row_sum;
for( size_t c = 0; c < no_branchings; ++c ){
size_t idx = matIndex( r, c, no_branchings );
row_sum += vars[idx];
}
model.addConstr( row_sum == 1.0 );
}
model.update();
// Optimize
model.optimize();
int status = model.get(GRB_IntAttr_Status);
if (status == GRB_OPTIMAL) {
std::cout << "The optimal objective is " << model.get(GRB_DoubleAttr_ObjVal) << std::endl;
// return results!
// printResults(vars, no_bones, no_branchings );
getResults( index_to_Bn, vars, assignments, no_bones, no_branchings );
return;
}
/************************************/
/* ERROR HANDLING */
/************************************/
if (status == GRB_UNBOUNDED){
std::cout << "The model cannot be solved because it is unbounded" << std::endl;
assert(false);
}
if ((status != GRB_INF_OR_UNBD) && (status != GRB_INFEASIBLE)) {
std::cout << "Optimization was stopped with status " << status << std::endl;
assert( false );
}
GRBConstr* c = 0;
// do IIS
std::cout << "The model is infeasible; computing IIS" << std::endl;
model.computeIIS();
std::cout << "\nThe following constraint(s) cannot be satisfied:" << std::endl;
c = model.getConstrs();
for (int i = 0; i < model.get(GRB_IntAttr_NumConstrs); ++i){
if (c[i].get(GRB_IntAttr_IISConstr) == 1) {
std::cout << c[i].get(GRB_StringAttr_ConstrName) << std::endl;
}
}
}
/* EXCEPTION HANDLING */
catch (GRBException e) {
std::cout << "Error code = " << e.getErrorCode() << std::endl;
std::cout << e.getMessage() << std::endl;
assert( false );
}
catch (...){
std::cout << "Exception during optimization" << std::endl;
assert( false );
}
}
int main(int argc, char *argv[])
{
GRBEnv* env = 0;
GRBVar* covered = 0; // we want to maximize demand covered
GRBVar* facility = 0;
int max_n_Facilities = 10;
try
{
const string filename = "/Users/katarzyna/Dropbox/matlab/2015-05 PKITS/aij_matrix.txt";
std::vector<std::vector<long int> > aij_matrix;
readMatrix_aij(filename, aij_matrix);
std::cout << "matrix size "<< aij_matrix.size() <<" x "<< aij_matrix[0].size() << std::endl;
// Model
env = new GRBEnv();
GRBModel model = GRBModel(*env);
model.set(GRB_StringAttr_ModelName, "minimize number of facilities");
// Demand coverage decision variable, covered[i] == 1 if demand is covered
covered = model.addVars(aij_matrix.size(), GRB_BINARY);
model.update();
// Facility open variables: facility[j] == 1 if facility j is open.
facility = model.addVars(aij_matrix.size(), GRB_BINARY);
model.update();
// Set the objective to maximize the demand covered
for (unsigned int i = 0; i < aij_matrix.size(); ++i)
{
ostringstream vname;
vname << "Demand " << i;
covered[i].set(GRB_DoubleAttr_Obj, 1);
covered[i].set(GRB_StringAttr_VarName, vname.str());
}
for (unsigned int i = 0; i < aij_matrix.size(); ++i)
{
ostringstream vname;
vname << "Open " << i;
facility[i].set(GRB_DoubleAttr_Obj, 0);
facility[i].set(GRB_StringAttr_VarName, vname.str());
}
// The objective is to maximize the total demand covered
model.set(GRB_IntAttr_ModelSense, 0);
// Update model
model.update();
// Constraints
// If demand i is covered by at least one station, then covered[i] == 1
for (unsigned int i = 0; i < aij_matrix.size(); ++i)
{
GRBLinExpr demand_is_covered = 0;
for (unsigned int j = 0; j < aij_matrix.size(); ++j)
{
demand_is_covered += aij_matrix[i][j] * facility[j];
}
ostringstream cname;
cname << "Demand_i_is_covered " << i;
model.addConstr(demand_is_covered >= covered[i], cname.str());
}
// We allow no more than n facilities
GRBLinExpr facilities_to_open = 0;
for (unsigned int j = 0; j < aij_matrix.size(); ++j) {
facilities_to_open += facility[j];
}
ostringstream vname;
vname << "Max facility... ";
model.addConstr(facilities_to_open <= max_n_Facilities);
model.update();
// Solve
model.optimize();
model.write("/Users/katarzyna/Dropbox/matlab/2015-05 PKITS/output.lp");
// Print solution
cout << "\nTOTAL COSTS: " << model.get(GRB_DoubleAttr_ObjVal) << endl;
cout << "SOLUTION:" << endl;
int sum_d = 0;
for (unsigned int i = 0; i < aij_matrix.size(); ++i)
{
if (covered[i].get(GRB_DoubleAttr_X) == 1.0)
{
//cout << "Demand " << i << " covered." << endl;
sum_d += 1;
}
}
int sum_f = 0;
for (unsigned int i = 0; i < aij_matrix.size(); ++i)
{
if (facility[i].get(GRB_DoubleAttr_X) == 1.0)
{
cout << "Facility " << i << " is opened." << endl;
sum_f += 1;
}
}
cout << sum_f << " facilities is open." << endl;
cout << sum_d << " customers is covered." << endl;
} catch (GRBException e)
{
cout << "Error code = " << e.getErrorCode() << endl;
cout << e.getMessage() << endl;
} catch (...)
{
cout << "Exception during optimization" << endl;
}
}
// ATENÇÃO: Não modifique a assinatura deste método.
bool brach_and_bound999999(TSP_Data_R &tsp, const vector<DNode> &terminais, const vector<DNode> &postos,
const DNode source,
int delta, int maxTime, vector<DNode> &sol, double &lbound){
// Converte o TSP direcionado para um nao direcionado com duas arestas
ListGraph graph;
EdgeValueMap weights(graph);
// Adiciona os nos
for (ListDigraph::NodeIt u(tsp.g); u!=INVALID; ++u)
{
Node v = graph.addNode();
}
// Adiciona as arestas
for (ListDigraph::ArcIt ait(tsp.g); ait!=INVALID; ++ait)
{
// pega os dois nos incidentes
Arc a(ait);
DNode u = tsp.g.source(a);
DNode v = tsp.g.target(a);
// cria a mesma aresta no grafo não direcionado
Node gu = graph.nodeFromId(tsp.g.id(u));
Node gv = graph.nodeFromId(tsp.g.id(v));
// insere a aresta no grafo nao direcionado
Edge e = graph.addEdge(gu, gv);
// Atribui pesos as arestas
weights[e] = tsp.weight[a];
}
NodeStringMap nodename(graph);
NodePosMap posicaox(graph);
NodePosMap posicaoy(graph);
TSP_Data utsp(graph, nodename, posicaox, posicaoy, weights);
// utiliza o convertido
ListGraph::EdgeMap<GRBVar> x(graph);
GRBEnv env = GRBEnv();
GRBModel model = GRBModel(env);
// TODO: [Opcional] Comente a linha abaixo caso não queira inserir cortes durante a execução do B&B
model.getEnv().set(GRB_IntParam_LazyConstraints, 1);
model.getEnv().set(GRB_IntParam_Seed, 0);
model.set(GRB_StringAttr_ModelName, "TSPR - TSP with Refueling"); // name to the problem
model.set(GRB_IntAttr_ModelSense, GRB_MINIMIZE); // is a minimization problem
// Add one binary variable for each arc and also sets its cost in the objective function
for (EdgeIt e(utsp.g); e!=INVALID; ++e) {
char name[100];
Edge edge(e);
unsigned uid = utsp.g.id(utsp.g.u(edge));
unsigned vid = utsp.g.id(utsp.g.v(edge));
sprintf(name,"x_%s_%s",tsp.vname[tsp.g.nodeFromId(uid)].c_str(),tsp.vname[tsp.g.nodeFromId(vid)].c_str());
x[e] = model.addVar(0.0, 1.0, utsp.weight[e],GRB_BINARY,name);
}
model.update(); // run update to use model inserted variables
// converte os terminais e os postos
vector<Node> uterminais;
for (auto t : terminais)
{
unsigned tid = tsp.g.id(t);
uterminais.push_back(utsp.g.nodeFromId(tid));
}
NodeBoolMap upostos(utsp.g, false);
for (auto p: postos)
{
unsigned pid = tsp.g.id(p);
// upostos.push_back(utsp.g.nodeFromId(pid));
upostos[utsp.g.nodeFromId(pid)] = true;
}
// Adicione restrições abaixo
// (1) Nós terminais devem ser visitados exatamente uma vez
for (auto v : uterminais) {
GRBLinExpr expr = 0;
for (IncEdgeIt e(utsp.g,v); e!=INVALID; ++e){
expr += x[e];
}
model.addConstr(expr == 2 );
}
// (3) Nó source sempre presente no início do caminho
Node usource = utsp.g.nodeFromId(tsp.g.id(source));
GRBLinExpr expr = 0;
for (IncEdgeIt e(utsp.g,usource); e!=INVALID; ++e){
expr += x[e];
}
model.addConstr(expr >= 1 );
try {
model.update(); // Process any pending model modifications.
//if (maxTime >= 0) model.getEnv().set(GRB_DoubleParam_TimeLimit,maxTime);
subtourelim cb = subtourelim(utsp , x, usource, upostos, delta);
model.setCallback(&cb);
// TODO: [Opcional] Pode-se utilizar o valor de uma solução heurística p/ acelerar o algoritmo B&B (cutoff value).
//cutoff = tsp.BestCircuitValue-MY_EPS;
double cutoff = 0.0;
if (cutoff > MY_EPS) model.getEnv().set(GRB_DoubleParam_Cutoff, cutoff );
model.update(); // Process any pending model modifications.
model.optimize();
// Obtém o status da otimização
int status = model.get(GRB_IntAttr_Status);
if(status == GRB_INFEASIBLE || status == GRB_INF_OR_UNBD){
cout << "Modelo inviavel ou unbounded." << endl;
return false;
}
// Limitante inferior e superior do modelo
//lbound = model.get(GRB_DoubleAttr_ObjBoundC);
if( model.get(GRB_IntAttr_SolCount) <= 0 ){
cout << "Modelo nao encontrou nenhuma solucao viavel no tempo. LowerBound = " << lbound << endl;
return false;
}
else if (status == GRB_OPTIMAL){
if(verbose) cout << "O modelo foi resolvido ate a otimalidade." << endl;
}
else {
if(verbose) cout << "O modelo encontrou uma solucao sub-otima (i.e. nao ha garantia de otimalidade)." << endl;
}
double custo_solucao = model.get(GRB_DoubleAttr_ObjVal);
int uncovered=0;
EdgeBoolMap cover(utsp.g, false);
for (EdgeIt e(utsp.g); e!=INVALID; ++e)
{
if (BinaryIsOne(x[e].get(GRB_DoubleAttr_X)))
{
cover[e] = true;
uncovered++;
}
}
sol.push_back(tsp.g.nodeFromId(utsp.g.id(usource)));
convertSol(x, sol, tsp, utsp, usource, cover, uncovered);
// Calculo manual do custo da solução (deve ser igual ao ObjVal do Gurobi).
double soma=0.0;
ArcName aname(tsp.g);
vector<Arc> edgesSol;
ArcColorMap acolor(tsp.g);
// if( verbose ) cout << "####### " << endl << "Edges of Solution (B&B):" << endl;
// for (EdgeIt e(utsp.g); e!=INVALID; ++e){
// if (BinaryIsOne(x[e].get(GRB_DoubleAttr_X))){ // Note que se este método serve para variáveis binárias, p/ inteiras terá de usar outro método.
// soma += utsp.weight[e];
// edgesSol.push_back(tsp.g.arcFromId(utsp.g.id(e)));
// if( verbose) cout << "(" << tsp.vname[tsp.g.nodeFromId(utsp.g.id(utsp.g.u(e)))] << "," << tsp.vname[tsp.g.nodeFromId(utsp.g.id(utsp.g.v(e)))] << ")" << endl;
// acolor[tsp.g.arcFromId(utsp.g.id(e))] = BLUE;
// }
// }
// if( verbose ) cout << "####### " << endl;
if( verbose ) cout << "####### " << endl << "Edges of Solution (B&B):" << endl;
DNode u = sol[0];
for (int i=1; i<sol.size(); i++)
{
DNode v = sol[i];
soma += tsp.AdjMatD.Cost(u,v);
if ( verbose ) cout << "(" << tsp.vname[u] << "," << tsp.vname[v] << ")" << endl;
u = v;
}
if( verbose ) cout << "####### " << endl;
if( verbose ) cout << "Custo calculado pelo B&B = "<< soma << " / " << custo_solucao << endl;
if( verbose ){
cout << "Caminho encontrado a partir do vértice de origem (" << tsp.vname[source] << "): ";
for(auto node : sol){
cout << tsp.vname[node] << " ";
} // Obs: O caminho é gerado a partir do nó source, se o conjunto de arestas retornado pelo B&B for desconexo, o caminho retornado por 'path_search' será incompleto.
cout << endl << "Custo calculado da solucao (caminho a partir do no origem) = " << solutionCost(tsp, sol) << endl;
ostringstream out;
out << "TSP with Refueling B&B, cost= " << custo_solucao;
ViewListDigraph(tsp.g, tsp.vname, tsp.posx, tsp.posy, tsp.vcolor, acolor, out.str());
}
return true;
}
catch(GRBException e) {
cerr << "Gurobi exception has been thrown." << endl;
cerr << "Error code = " << e.getErrorCode() << endl;
cerr << e.getMessage();
}
catch (...) {
cout << "Model is infeasible" << endl;
return false;
}
return false;
}
int main(int argc, char *argv[])
{
int time_limit;
char name[1000];
double cutoff=0.0;
ListGraph g;
EdgeWeight lpvar(g);
EdgeWeight weight(g);
NodeName vname(g);
ListGraph::NodeMap<double> posx(g),posy(g);
string filename;
int seed=1;
// uncomment one of these lines to change default pdf reader, or insert new one
//set_pdfreader("open"); // pdf reader for Mac OS X
//set_pdfreader("xpdf"); // pdf reader for Linux
//set_pdfreader("evince"); // pdf reader for Linux
srand48(seed);
time_limit = 3600; // solution must be obtained within time_limit seconds
if (argc!=2) {cout<< endl << "Usage: "<< argv[0]<<" <graph_filename>"<<endl << endl <<
"Example: " << argv[0] << " gr_berlin52" << endl <<
" " << argv[0] << " gr_att48" << endl << endl; exit(0);}
else if (!FileExists(argv[1])) {cout<<"File "<<argv[1]<<" does not exist."<<endl; exit(0);}
filename = argv[1];
// Read the graph
if (!ReadListGraph(filename,g,vname,weight,posx,posy))
{cout<<"Error reading graph file "<<argv[1]<<"."<<endl;exit(0);}
TSP_Data tsp(g,vname,posx,posy,weight);
ListGraph::EdgeMap<GRBVar> x(g);
GRBEnv env = GRBEnv();
GRBModel model = GRBModel(env);
#if GUROBI_NEWVERSION
model.getEnv().set(GRB_IntParam_LazyConstraints, 1);
model.getEnv().set(GRB_IntParam_Seed, seed);
#else
model.getEnv().set(GRB_IntParam_DualReductions, 0); // Dual reductions must be disabled when using lazy constraints
#endif
model.set(GRB_StringAttr_ModelName, "Undirected TSP with GUROBI"); // name to the problem
model.set(GRB_IntAttr_ModelSense, GRB_MINIMIZE); // is a minimization problem
// Add one binary variable for each edge and also sets its cost in the objective function
for (ListGraph::EdgeIt e(g); e!=INVALID; ++e) {
sprintf(name,"x_%s_%s",vname[g.u(e)].c_str(),vname[g.v(e)].c_str());
x[e] = model.addVar(0.0, 1.0, weight[e],GRB_BINARY,name);
}
model.update(); // run update to use model inserted variables
// Add degree constraint for each node (sum of solution edges incident to a node is 2)
for (ListGraph::NodeIt v(g); v!=INVALID; ++v) {
GRBLinExpr expr;
for (ListGraph::IncEdgeIt e(g,v); e!=INVALID; ++e) expr += x[e];
//aqui model.addConstr(expr == 2 ); what? ignorou!
model.addConstr(expr == 2 );
}
try {
model.update(); // Process any pending model modifications.
if (time_limit >= 0) model.getEnv().set(GRB_DoubleParam_TimeLimit,time_limit);
subtourelim cb = subtourelim(tsp , x);
model.setCallback(&cb);
tsp.max_perturb2opt_it = 200; //1000; // number of iterations used in heuristic TSP_Perturb2OPT
TSP_Perturb2OPT(tsp);
if (tsp.BestCircuitValue < DBL_MAX) cutoff = tsp.BestCircuitValue-BC_EPS; //
// optimum value for gr_a280=2579, gr_xqf131=566.422, gr_drilling198=15808.652
if (cutoff > 0) model.getEnv().set(GRB_DoubleParam_Cutoff, cutoff );
model.update(); // Process any pending model modifications.
model.optimize();
double soma=0.0;
for (ListGraph::EdgeIt e(g); e!=INVALID; ++e) {
lpvar[e] = x[e].get(GRB_DoubleAttr_X);
if (lpvar[e] > 1-BC_EPS ) {
soma += weight[e];
if (
(vname[g.u(e)] == "243")||(vname[g.v(e)] == "243") ||
(vname[g.u(e)] == "242")||(vname[g.v(e)] == "242")
) {
cout << "Achei, x("<< vname[g.u(e)] << " , " << vname[g.v(e)] << " = " << lpvar[e] <<"\n";
}
}
}
cout << "Solution cost = "<< soma << endl;
Update_Circuit(tsp,x); // Update the circuit in x to tsp circuit variable (if better)
ViewTspCircuit(tsp);
}catch (...) {
if (tsp.BestCircuitValue < DBL_MAX) {
cout << "Heuristic obtained optimum solution" << endl;
ViewTspCircuit(tsp);
return 0;
}else {
cout << "Graph is infeasible" << endl;
return 1;
}
}
}
int main(){
float peso1, peso2, peso3,peso4;
int origem, destino; // vértices para cada aresta;
int id = 0; // id das arestas que leremos do arquivo para criar o grafo
cin>>n; // quantidade de vértices do grafo;
arestas = new short*[n];
coeficienteObjetv = new double*[n];
matrix_peso1 = new double*[n];
matrix_peso2 = new double*[n];
matrix_peso3 = new double*[n];
matrix_peso4 = new double*[n];
for (int i=0; i<n; i++){
arestas[i] = new short[n];
coeficienteObjetv[i] = new double[n];
matrix_peso1[i] = new double[n];
matrix_peso2[i] = new double[n];
matrix_peso3[i] = new double[n];
matrix_peso4[i] = new double[n];
}
GRBEnv env = GRBEnv();;
env.set("OutputFlag","0");
GRBModel model = GRBModel(env);;
GRBVar **y, **x;
float epslon = 0.0001;
//cin>>epslon;
y = new GRBVar*[n];
x = new GRBVar*[n];
for (int i=0; i<n;i++){
y[i] = new GRBVar[n];
x[i] = new GRBVar[n];
}
int constrCont=0;
// Create variables
for (int i=0; i<n; i++){
for (int j=0; j<n; j++){
arestas[i][j] = 0;
}
}
while (cin>>origem){
cin>>destino;
cin>>peso1;
cin>>peso2;
cin>>peso3;
cin>>peso4;
coeficienteObjetv[origem][destino] = (peso1*epslon + peso2*epslon + peso3*epslon + peso4)*(-1); // o problema é de maximizacao
x[origem][destino] = model.addVar(0.0, 100000, 0.0, GRB_CONTINUOUS, "x"+to_string(origem)+to_string(destino));
x[destino][origem] = model.addVar(0.0, 100000, 0.0, GRB_CONTINUOUS, "x"+to_string(destino)+to_string(origem));
y[origem][destino] = model.addVar(0.0, 1.0, 0.0, GRB_BINARY, "y"+to_string(origem)+to_string(destino));
arestas[origem][destino] = 1;
arestas[destino][origem] = 1;
matrix_peso1[origem][destino] = peso1*(-1);
matrix_peso2[origem][destino] = peso2*(-1);
matrix_peso3[origem][destino] = peso3*(-1);
matrix_peso4[origem][destino] = peso4*(-1);
id++;
}
int nA = id; // quantidade de arestas do grafo
int m = 1;// por default, o m falado por Lokman and Koksalan sera igual a 1
model.update();
// Set objective:
GRBLinExpr exprObjet;
for (int i=0; i<n; i++){
for (int j=i+1; j<n; j++){
if (arestas[i][j] == 1)
exprObjet.addTerms(&coeficienteObjetv[i][j], &y[i][j],1);
}
}
model.setObjective(exprObjet,GRB_MAXIMIZE);
// constraint 3.9 (FERNANDES, 2016)
GRBLinExpr constr5 ;
double coefff = 1;
for (int j=0+1; j<n; j++){
if (arestas[0][j] == 1)
constr5.addTerms(&coefff,&x[0][j],1);
}
model.addConstr(constr5, GRB_EQUAL, n-1,to_string(constrCont++));
// // Add constraint 3.10 (FERNANDES, 2016)
double com = -1;
for (int j=1; j<n; j++){
GRBLinExpr constr2 = 0;
for (int i=0; i<n; i++){
if (arestas[i][j] == 1){
constr2.addTerms(&coefff,&x[i][j],1);
constr2.addTerms(&com,&x[j][i],1);
}
}
model.addConstr(constr2, GRB_EQUAL, 1,to_string(constrCont++));
}
double coef = (double) n - 1;
for (int i=0; i<n; i++){
for (int j=i+1; j<n; j++){
if (arestas[i][j] == 1){
GRBLinExpr constr8;
GRBLinExpr constr9;
constr8.addTerms(&coef,&y[i][j],1);
constr9.addTerms(&coefff ,&x[i][j],1);
constr9.addTerms(&coefff ,&x[j][i],1);
model.addConstr(constr8, GRB_GREATER_EQUAL, constr9,to_string(constrCont++));
}
}
}
//cout<<"Modelo carregado"<<endl;
for (int i=0; i<n; i++){
for (int j=i+1; j<n; j++){
if (arestas[i][j] == 1){
GRBLinExpr constr22;
GRBLinExpr constr33;
constr22.addTerms(&coefff ,&y[i][j],1);
constr33.addTerms(&coefff ,&x[i][j],1);
constr33.addTerms(&coefff ,&x[j][i],1);
// cout<<constr22<<GRB_LESS_EQUAL<<constr33<<endl;
model.addConstr(constr22, GRB_LESS_EQUAL, constr33,to_string(constrCont++));
}
}
}
int nn = 0; // o 'n' do algoritmo de Lokman and Koksalan
//int kk_estrela = 0; // o 'k*' do algoritmo 2 de Lokman and Koksalan
int MM = 100000000; // o 'M' do algoritmo 2 de Lokman and Koksalan
int z4_k_estrela; // pra guardar o Z_p^(P^(b^(k*,n))) do algoritmo 2 de Lokman and Koksalan
/*
* Algoritmo 2 de Lokman and Koksalan
*/
try {
times(&tempsInit);
// para medir o tempo em caso limite
pthread_t thread_time;
pthread_attr_t attr;
int nnnnnnnn=0;
if(pthread_create(&thread_time, NULL, &tempo, (void*)nnnnnnnn)){ // on criee efectivement la thread de rechaufage
cout<<"Error to create the thread"<<endl;
exit(EXIT_FAILURE);
}
//
bool auxbol = false; // vira true (e o será pra sempre) quando resolvemos um modelo diferente do SIMPLES
int optimstatus;
short **result = new short*[n];
for (int ii=0; ii<n; ii++){
result[ii] = new short[n];
}
model.optimize(); // P0,4 --> n=0 (modelo SIMPLES)
optimstatus = model.get(GRB_IntAttr_Status);
int z1=0,z2=0,z3=0,z4=0;
if (optimstatus != GRB_INFEASIBLE){
for (int i=0; i<n; i++){
for (int j=i+1; j<n; j++){
if (arestas[i][j] == 1){
result[i][j] = y[i][j].get(GRB_DoubleAttr_X); // GUARDA O RESULTADO
z1+=result[i][j]*matrix_peso1[i][j]; // calcula os pesos
z2+=result[i][j]*matrix_peso2[i][j];
z3+=result[i][j]*matrix_peso3[i][j];
z4+=result[i][j]*matrix_peso4[i][j];
}
}
}
S.push_back(result);
Pesos ppp = (Pesos){z1,z2,z3,z4};
Z.push_back(ppp);
nn++;
}
do{ // esse loop para quando z4_k_estrela==-MM
z4_k_estrela =(-1)*MM; // guarda o maximo
short **z_n_plus_1 = new short*[n];
for (int ii=0; ii<n; ii++){
z_n_plus_1[ii] = new short[n];
}
int z1_estrela, z2_estrela, z3_estrela;
for (int ki=-1; ki<nn; ki++){ // no algoritmo original, ki deve variar de 0 à n (existindo solucoes de 1 à n).
//aqui, portanto, fazemos k de -1 à n-1, porque as solucoes vao de 0 à n-1
for (int kj=-1; kj<nn; kj++){ // como i de ver menor que j, a unica possibilidade é i=1 e j=2, pois p-2=2
//cout<<ki<<" "<<kj<<endl;
//cout<< Z[ki].peso1<<" "<< Z[kj].peso2<<endl;
//if (kj!=-1 && ki!=-1 && (Z[ki].peso1 + 1 <= Z[kj].peso1)){
//Primeiramente, prepara o b1 e b2 e b3
int b1,b2,b3;
//b1
if (ki==-1) b1=(-1)*MM; // -M
else {
b1 = Z[ki].peso1 + 1;
}
//b2
if (kj==-1) b2=(-1)*MM; // -M
else {
if (Z[kj].peso1 >= b1){
b2 = Z[kj].peso2 + 1;
} else b2=(-1)*MM; // -M
}
//b3
b3 = (-1)*MM; // Snk = vazio
for (int ii=0; ii<S.size(); ii++){
if (Z[ii].peso1>=b1 && Z[ii].peso2>=b2) {
if (Z[ii].peso3 > b3){
b3 = Z[ii].peso3;
}
}
}
if (b3!=(-1)*MM) b3=b3+1; // max + 1
//cout <<"b1= "<<b1<<" b2= "<<" "<<b2<<" b3= "<<b3<<endl;
if (auxbol == true){ // remove as restricoes de z2>b2 e adiciona novas
GRBConstr cb1 = model.getConstrByName("cb1");
GRBConstr cb2 = model.getConstrByName("cb2");
GRBConstr cb3 = model.getConstrByName("cb3");
model.remove(cb1);
model.remove(cb2);
model.remove(cb3);
}
GRBLinExpr cb1;
GRBLinExpr cb2;
GRBLinExpr cb3;
for (int i=0; i<n; i++){
for (int j=i+1; j<n; j++){
if (arestas[i][j] == 1){
cb1.addTerms(&matrix_peso1[i][j], &y[i][j],1);
cb2.addTerms(&matrix_peso2[i][j], &y[i][j],1);
cb3.addTerms(&matrix_peso3[i][j], &y[i][j],1);
}
}
}
model.addConstr(cb1, GRB_GREATER_EQUAL, b1,"cb1");
model.addConstr(cb2, GRB_GREATER_EQUAL, b2,"cb2");
model.addConstr(cb3, GRB_GREATER_EQUAL, b3,"cb3");
// AGORA RESOLVE-SE O MODELO
auxbol=true;
model.optimize();
optimstatus = model.get(GRB_IntAttr_Status);
if (optimstatus != GRB_INFEASIBLE){
short **result = new short*[n];
for (int ii=0; ii<n; ii++){
result[ii] = new short[n];
}
z1=0,z2=0,z3=0,z4=0;
for (int i=0; i<n; i++){
for (int j=i+1; j<n; j++){
if (arestas[i][j] == 1){
result[i][j] = y[i][j].get(GRB_DoubleAttr_X); // GUARDA O RESULTADO
z1+=result[i][j]*matrix_peso1[i][j]; // calcula os pesos
z2+=result[i][j]*matrix_peso2[i][j];
z3+=result[i][j]*matrix_peso3[i][j];
z4+=result[i][j]*matrix_peso4[i][j];
}
}
}
if (z4>z4_k_estrela){
z1_estrela = z1;
z2_estrela = z2;
z3_estrela = z3;
z4_k_estrela = z4;
for (int i=0; i<n; i++){
for (int j=i+1; j<n; j++){
z_n_plus_1[i][j] = result[i][j];
}
}
}
}
//}
}
}
if (z4_k_estrela!=(-1)*MM){
S.push_back(z_n_plus_1);
Pesos pppp = (Pesos){z1_estrela,z2_estrela,z3_estrela,z4_k_estrela};
Z.push_back(pppp);
nn++;
//cout<<"nn = "<<nn<<endl;
}
} while (z4_k_estrela!=(-1)*MM);
times(&tempsFinal1); /* current time */ // clock final
clock_t user_time1 = (tempsFinal1.tms_utime - tempsInit.tms_utime);
cout<<user_time1<<endl;
cout<<(float) user_time1 / (float) sysconf(_SC_CLK_TCK)<<endl;//"Tempo do usuario por segundo : "
cout<<"RESULTADO FINAL..."<<endl;
printResultado();
} catch(GRBException e) {
cout << "Error code = " << e.getErrorCode() << endl;
cout << e.getMessage() << endl;
} catch(...) {
cout << "Exception during optimization" << endl;
}
return 0;
}
vector <double> solve2(vector <vector <double> > M) {
vector <double> solution;
try {
GRBEnv env = GRBEnv();
vector <vector <double> > N = M;
int numPoints = M.size();
int numCircles = M.back().size();
//cout<<"solving for "<<numCircles<<" circles and "<<numPoints<<" points"<<endl;
GRBVar p[numPoints];
double coeff[numPoints];
GRBModel model = GRBModel(env);
GRBLinExpr expr;
// Create variables
for (int i=0;i<numPoints;i++){
p[i] = model.addVar(0.0, 1.0, 0.0, GRB_BINARY);
coeff[i] = 1.0;
}
expr.addTerms(coeff, p,numPoints);
// Integrate new variables
model.update();
// Set objective: maximize x + y + 2 z
model.setObjective(expr, GRB_MINIMIZE);
for(int i=0;i<numCircles;i++){
GRBLinExpr cexpr;
double ccoeff[numPoints];
for(int j=0;j<numPoints;j++){
ccoeff[j] = M[j].back();
M[j].pop_back();
}
cexpr.addTerms(ccoeff,p,numPoints);
model.addConstr(cexpr, GRB_GREATER_EQUAL,1.0);
}
// Optimize model
model.optimize();
int c=0;
for (int i=0;i<numPoints;i++){
solution.push_back((double)p[i].get(GRB_DoubleAttr_X));
c += solution.back();
}
model.addConstr(expr, GRB_EQUAL, c);
int temp;
int temp2;
for (int i=0;i<numPoints;i++){
temp=0;
while(N.back().size()){
temp = temp + (N.back()).back();
N.back().pop_back();
}
coeff[i] = temp;
N.pop_back();
}
expr.addTerms(coeff, p,numPoints);
if (rand()%2)
model.setObjective(expr, GRB_MINIMIZE);
else
model.setObjective(expr, GRB_MAXIMIZE);
model.optimize();
solution.clear();
c=0;
for (int i=0;i<numPoints;i++){
solution.push_back((double)p[i].get(GRB_DoubleAttr_X));
c += solution.back();
}
} catch(GRBException e) {
cout << "Error code = " << e.getErrorCode() << endl;
// cout << e.getMessage() << endl;
} catch(...) {
cout << "Exception during optimization" << endl;
}
return solution;
}
int main(int argc,char *argv[]) {
srand48(1);
if (argc!=2) {cout<<endl<<"Usage: "<< argv[0]<<" <filename>"<< endl << endl;
cout << "Example: " << argv[0] << " arq1.in" << endl << endl; exit(0);}
ifstream ifile;
ifile.open(argv[1]); if (!ifile) return(false);
int n, m;
ifile >> n; ifile >> m;
vector<double> jobsize(n);
vector<double> machinespeed(m);
for(int i=0; i<n; i++) ifile >> jobsize[i];
for(int i=0; i<m; i++) ifile >> machinespeed[i];
ifile.close();
cout << endl;
cout << "Numero de tarefas: " << n << endl;
cout << "Tamanho das tarefas" << endl;
for(int i=0; i<n; i++)
cout << "T_" << i << ": " << jobsize[i] << endl;
cout << "Velocidade das maquinas" << endl;
for(int i=0; i<m; i++)
cout << "M_" << i << ": " << machinespeed[i] << endl;
cout << endl << endl;
try {
/*--------------------------------------------------------------------------------- */
/*--------------------------- ALTERE DAQUI PARA ABAIXO ---------------------------- */
/*--------------------------------------------------------------------------------- */
// Voce deve atualizar esta variavel com valores 0 e 1 de tal forma que
// tarefa_maquina[i][j] = 1 SE_E_SOMENTE_SE a tarefa i foi escalonada na maq. j
vector<vector<int> > tarefa_maquina(n, vector<int>(m));
// mais abaixo eh feito um escalonamento aleatorio.
// Nao se esqueca de alterar a geracao do escalonamento aleatorio para o
// escalonamento obtido pelo seu programa linear inteiro
// cabecalho comum para os programas lineares (descomente)
int seed=0;
GRBEnv env = GRBEnv();
GRBModel model = GRBModel(env);
model.getEnv().set(GRB_IntParam_Seed, seed);
model.set(GRB_StringAttr_ModelName, "Escalonamento de Tarefas"); // prob. name
model.set(GRB_IntAttr_ModelSense, GRB_MINIMIZE); // is a minimization problem
// Lembretes e sintaxes de alguns comandos:
// GRBVar = nome do tipo das variaveis do Gurobi
//
vector<GRBVar> x[m];
double TP[m][n];
GRBLinExpr expr;
for(int i=0; i<m; i++) { //Para cada maquina
for(int j=0; j<n; j++) { //Para cada tarefa
char name[100];
sprintf(name,"T(%d)/M(%d)",j,i);
TP[i][j] = jobsize[j]/machinespeed[i];
x[i].push_back(model.addVar(0,1,TP[i][j],GRB_BINARY,name));
expr +=
//Preciso no momento minimizar m esprecoes em conjunto
}
aaa = -aaa;
}
// Para inserir uma variavel:
// <variavel_do_tipo_GRBVar> =
// model.addVar(<lower_bound>,
// <upper_bound> ,
// <valor_na_funcao_objetivo>,
// <GRB_CONTINUOUS ou GRB_BINARY GRB_INTEGER>
// <string_do_nome_da_variavel>);
//
// Este comando deve ser executado depois de inserir todas as variaveis e antes de
// inserir as restricoes.
model.update();
//
// Para declarar variavel que armazena expressoes
// for(int i = 0; i<n; i++) { //para cada maquina
// GRBLinExpr expr;
// for(int j = 0; j<m;j++) { //para cada tarefa
// expr+=x[m*i+j];
// }
// model.addConstr(expr >= 0);
// }
for(int i = 0; i<n; i++) { //para cada tarefa
GRBLinExpr expr;
for(int j = 0; j<m; j++) { //para cada maquina
expr += x[j][i];
}
model.addConstr(expr == 1); //uma maquina executa cada tarefa
}
//
// Para inserir uma restricao linear:
// model.addConstr( <restricao_linear> );
//
// Comando para escrever o modelo produzido em um arquivo texto (apenas para debugar)
model.update(); //Tem que dar update de novo pra ver as restricoes
model.write("model.lp"); system("cat model.lp");
//
// Depois que construiu seu programa linear / linear inteiro, execute este comando
// para resolver o programa linear
model.optimize();
//
// Verifica se obteve solucao otima
if (model.get(GRB_IntAttr_Status) != GRB_OPTIMAL) {
cout << "Erro, sistema impossivel" << endl;
exit(1);
}
//
// Para obter o valor da variavel do programa linear/linear inteiro
// deve ser executado apos o model.optimize()
// <variavel_do_tipo_GRBVar>.get(GRB_DoubleAttr_X)
// Faz um escalonamento aleatorio.
// Remova esta parte e coloque o escalonamento
// gerado pelo seu programa linear inteiro.
for (int i=0; i<n; i++)
for (int j=0; j<m; j++)
tarefa_maquina[i][j] = 0; // deixa cada maq. sem atribuicao
for (int i=0; i<n; i++)
for (int j=0; j<m; j++)
if(x[j][i].get(GRB_DoubleAttr_X) >= 0.999) {
cout << j << " " << i << endl;
tarefa_maquina[i][j] = 1;
}
/*--------------------------------------------------------------------------------- */
/*--------------------------- ALTERE DAQUI PARA CIMA ------------------------------ */
/*--------------------------------------------------------------------------------- */
cout << "\n\n";
double makespan=0;
cout << "Escalonamento obtido das tarefas nas maquinas\n";
cout << "Notacao para tarefas : T_id(tamanho_original)\n";
cout << "Notacao para maquinas: M_id(velocidade)\n\n";
for (int j=0; j<m; j++) {
double tmaq=0.0;
cout << "M_"<<j<<"("<< machinespeed[j] << ") : ";
for(int i=0; i<n; i++) {
if (tarefa_maquina[i][j] == 1) {
cout << "T_" << i << "(" << jobsize[i] << ") ";
tmaq += jobsize[i] / machinespeed[j];
}
}
cout << endl << "Tempo gasto pela maquina M_" <<j << ": " << tmaq << endl<<endl;
if (tmaq>makespan) makespan = tmaq;
}
cout << "\nTempo para completar todas as tarefas: " << makespan << "s\n\n";
// } else cout << "No solution" << "\n";
}
// catch (GRBException e) {
// cout << "Error code = " << e.getErrorCode() << endl;
// cout << e.getMessage() << endl;
// }
catch (...) {
cout << "Exception during optimization" << endl;
}
return 0;
}
void solve(Instance inst, bool hsol = false){
clock_t t0 = CURTIME;
ArcflowMKP graph(inst);
const vector<Item> &items = inst.items;
GRBEnv* env = new GRBEnv();
GRBModel master = GRBModel(*env);
master.set(GRB_StringAttr_ModelName, "GG");
master.getEnv().set(GRB_IntParam_OutputFlag, 0);
master.getEnv().set(GRB_IntParam_Threads, 1);
master.getEnv().set(GRB_IntParam_Method, 0);
GRBConstr rows[inst.m];
for(int i = 0; i < inst.m; i++){
GRBLinExpr lin = 0;
rows[i] = master.addConstr(lin >= items[i].demand);
}
master.update();
vector<GRBVar> vars;
for(int i = 0; i < inst.m; i++){
GRBColumn col = GRBColumn();
col.addTerm(1, rows[i]);
vars.push_back(master.addVar(0, GRB_INFINITY, 1, GRB_CONTINUOUS, col));
}
printf("m: %d\n", inst.m);
vector<double> values(inst.m);
for(int itr = inst.m; ; itr++){
master.optimize();
printf("%d: %.6f (%.2fs)\n", itr, master.get(GRB_DoubleAttr_ObjVal), TIMEDIF(t0));
for(int i = 0; i < inst.m; i++)
values[i] = rows[i].get(GRB_DoubleAttr_Pi);
vector<int_pair> sol = graph.knapsack(values, 1+EPSILON);
if(sol.empty()) break;
GRBColumn col = GRBColumn();
ForEach(itr, sol) col.addTerm(itr->second, rows[itr->first]);
vars.push_back(master.addVar(0, GRB_INFINITY, 1, GRB_CONTINUOUS, col));
master.update();
}
printf("zlp: %.6f\n", master.get(GRB_DoubleAttr_ObjVal));
printf("nvars: %d\n", master.get(GRB_IntAttr_NumVars));
printf("time: %.2fs\n", TIMEDIF(t0));
if(hsol){ // find an heuristic solution if hsol = true
ForEach(itr, vars)
itr->set(GRB_CharAttr_VType, GRB_INTEGER);
master.getEnv().set(GRB_IntParam_OutputFlag, 1);
master.getEnv().set(GRB_IntParam_Threads, 1);
//master.getEnv().set(GRB_IntParam_Presolve, 1);
//master.getEnv().set(GRB_IntParam_Method, 2);
master.getEnv().set(GRB_IntParam_MIPFocus, 1);
master.getEnv().set(GRB_DoubleParam_Heuristics, 1);
master.getEnv().set(GRB_DoubleParam_MIPGap, 0);
master.getEnv().set(GRB_DoubleParam_MIPGapAbs, 1-1e-5);
master.optimize();
printf("Total run time: %.2f seconds\n", TIMEDIF(t0));
}
free(env);
}
int
main(int argc,
char *argv[])
{
if (argc < 2) {
cout << "Usage: tsp_c++ filename" << endl;
return 1;
}
int n = atoi(argv[1]);
double* x = new double[n];
double* y = new double[n];
for (int i = 0; i < n; i++) {
x[i] = ((double) rand())/RAND_MAX;
y[i] = ((double) rand())/RAND_MAX;
}
GRBEnv *env = NULL;
GRBVar **vars = new GRBVar*[n];
try {
int i, j;
env = new GRBEnv();
GRBModel model = GRBModel(*env);
// Must disable dual reductions when using lazy constraints
model.getEnv().set(GRB_IntParam_DualReductions, 0);
// Create binary decision variables
for (i = 0; i < n; i++)
vars[i] = model.addVars(n);
model.update();
for (i = 0; i < n; i++) {
for (j = 0; j < n; j++) {
vars[i][j].set(GRB_CharAttr_VType, GRB_BINARY);
vars[i][j].set(GRB_DoubleAttr_Obj, distance(x, y, i, j));
vars[i][j].set(GRB_StringAttr_VarName, "x_"+itos(i)+"_"+itos(j));
}
}
// Integrate new variables
model.update();
// Degree-2 constraints
for (i = 0; i < n; i++) {
GRBLinExpr expr = 0;
for (j = 0; j < n; j++)
expr += vars[i][j];
model.addConstr(expr == 2, "deg2_"+itos(i));
}
// Forbid edge from node back to itself
for (i = 0; i < n; i++)
vars[i][i].set(GRB_DoubleAttr_UB, 0);
// Symmetric TSP
for (i = 0; i < n; i++)
for (j = 0; j < i; j++)
model.addConstr(vars[i][j] == vars[j][i]);
// Set callback function
subtourelim cb = subtourelim(vars, n);
model.setCallback(&cb);
// Optimize model
model.optimize();
// Extract solution
if (model.get(GRB_IntAttr_SolCount) > 0) {
double **sol = new double*[n];
for (i = 0; i < n; i++)
sol[i] = model.get(GRB_DoubleAttr_X, vars[i], n);
int* tour = new int[n];
int len;
findsubtour(n, sol, &len, tour);
cout << "Tour: ";
for (i = 0; i < len; i++)
cout << tour[i] << " ";
cout << endl;
for (i = 0; i < n; i++)
delete[] sol[i];
delete[] sol;
delete[] tour;
}
} catch (GRBException e) {
cout << "Error number: " << e.getErrorCode() << endl;
cout << e.getMessage() << endl;
} catch (...) {
cout << "Error during optimization" << endl;
}
for (int i = 0; i < n; i++)
delete[] vars[i];
delete[] vars;
delete[] x;
delete[] y;
delete env;
return 0;
}
int
main(int argc,
char *argv[])
{
GRBEnv* env = 0;
GRBVar* open = 0;
GRBVar** transport = 0;
int transportCt = 0;
try
{
// Number of plants and warehouses
const int nPlants = 5;
const int nWarehouses = 4;
// Warehouse demand in thousands of units
double Demand[] = { 15, 18, 14, 20 };
// Plant capacity in thousands of units
double Capacity[] = { 20, 22, 17, 19, 18 };
// Fixed costs for each plant
double FixedCosts[] = { 12000, 15000, 17000, 13000, 16000 };
// Transportation costs per thousand units
double TransCosts[][nPlants] = {
{ 4000, 2000, 3000, 2500, 4500 },
{ 2500, 2600, 3400, 3000, 4000 },
{ 1200, 1800, 2600, 4100, 3000 },
{ 2200, 2600, 3100, 3700, 3200 }
};
// Model
env = new GRBEnv();
GRBModel model = GRBModel(*env);
model.set(GRB_StringAttr_ModelName, "facility");
// Plant open decision variables: open[p] == 1 if plant p is open.
open = model.addVars(nPlants, GRB_BINARY);
model.update();
int p;
for (p = 0; p < nPlants; ++p)
{
ostringstream vname;
vname << "Open" << p;
open[p].set(GRB_DoubleAttr_Obj, FixedCosts[p]);
open[p].set(GRB_StringAttr_VarName, vname.str());
}
// Transportation decision variables: how much to transport from a plant p to a warehouse w
transport = new GRBVar* [nWarehouses];
int w;
for (w = 0; w < nWarehouses; ++w)
{
transport[w] = model.addVars(nPlants);
transportCt++;
model.update();
for (p = 0; p < nPlants; ++p)
{
ostringstream vname;
vname << "Trans" << p << "." << w;
transport[w][p].set(GRB_DoubleAttr_Obj, TransCosts[w][p]);
transport[w][p].set(GRB_StringAttr_VarName, vname.str());
}
}
// The objective is to minimize the total fixed and variable costs
model.set(GRB_IntAttr_ModelSense, 1);
// Update model to integrate new variables
model.update();
// Production constraints
// Note that the right-hand limit sets the production to zero if
// the plant is closed
for (p = 0; p < nPlants; ++p)
{
GRBLinExpr ptot = 0;
for (w = 0; w < nWarehouses; ++w)
{
ptot += transport[w][p];
}
ostringstream cname;
cname << "Capacity" << p;
model.addConstr(ptot <= Capacity[p] * open[p], cname.str());
}
// Demand constraints
for (w = 0; w < nWarehouses; ++w)
{
GRBLinExpr dtot = 0;
for (p = 0; p < nPlants; ++p)
{
dtot += transport[w][p];
}
ostringstream cname;
cname << "Demand" << w;
model.addConstr(dtot == Demand[w], cname.str());
}
// Guess at the starting point: close the plant with the highest
// fixed costs; open all others
// First, open all plants
for (p = 0; p < nPlants; ++p)
{
open[p].set(GRB_DoubleAttr_Start, 1.0);
}
// Now close the plant with the highest fixed cost
cout << "Initial guess:" << endl;
double maxFixed = -GRB_INFINITY;
for (p = 0; p < nPlants; ++p)
{
if (FixedCosts[p] > maxFixed)
{
maxFixed = FixedCosts[p];
}
}
for (p = 0; p < nPlants; ++p)
{
if (FixedCosts[p] == maxFixed)
{
open[p].set(GRB_DoubleAttr_Start, 0.0);
cout << "Closing plant " << p << endl << endl;
break;
}
}
// Use barrier to solve root relaxation
model.getEnv().set(GRB_IntParam_Method, GRB_METHOD_BARRIER);
// Solve
model.optimize();
// Print solution
cout << "\nTOTAL COSTS: " << model.get(GRB_DoubleAttr_ObjVal) << endl;
cout << "SOLUTION:" << endl;
for (p = 0; p < nPlants; ++p)
{
if (open[p].get(GRB_DoubleAttr_X) == 1.0)
{
cout << "Plant " << p << " open:" << endl;
for (w = 0; w < nWarehouses; ++w)
{
if (transport[w][p].get(GRB_DoubleAttr_X) > 0.0001)
{
cout << " Transport " << transport[w][p].get(GRB_DoubleAttr_X) << " units to warehouse " << w << endl;
}
}
}
else
{
cout << "Plant " << p << " closed!" << endl;
}
}
}
catch (GRBException e)
{
cout << "Error code = " << e.getErrorCode() << endl;
cout << e.getMessage() << endl;
}
catch (...)
{
cout << "Exception during optimization" << endl;
}
delete[] open;
for (int i = 0; i < transportCt; ++i) {
delete[] transport[i];
}
delete[] transport;
delete env;
return 0;
}
int
main(int argc,
char *argv[])
{
GRBEnv* env = 0;
GRBConstr* c = 0;
GRBVar* v = 0;
GRBVar** x = 0;
GRBVar* slacks = 0;
GRBVar* totShifts = 0;
GRBVar* diffShifts = 0;
int xCt = 0;
try
{
// Sample data
const int nShifts = 14;
const int nWorkers = 7;
// Sets of days and workers
string Shifts[] =
{ "Mon1", "Tue2", "Wed3", "Thu4", "Fri5", "Sat6",
"Sun7", "Mon8", "Tue9", "Wed10", "Thu11", "Fri12", "Sat13",
"Sun14" };
string Workers[] =
{ "Amy", "Bob", "Cathy", "Dan", "Ed", "Fred", "Gu" };
// Number of workers required for each shift
double shiftRequirements[] =
{ 3, 2, 4, 4, 5, 6, 5, 2, 2, 3, 4, 6, 7, 5 };
// Worker availability: 0 if the worker is unavailable for a shift
double availability[][nShifts] =
{ { 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1 },
{ 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0 },
{ 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1 },
{ 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1 },
{ 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1 },
{ 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1 },
{ 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 } };
// Model
env = new GRBEnv();
GRBModel model = GRBModel(*env);
model.set(GRB_StringAttr_ModelName, "assignment");
// Assignment variables: x[w][s] == 1 if worker w is assigned
// to shift s. This is no longer a pure assignment model, so we must
// use binary variables.
x = new GRBVar*[nWorkers];
for (int w = 0; w < nWorkers; ++w)
{
x[w] = model.addVars(nShifts);
xCt++;
model.update();
for (int s = 0; s < nShifts; ++s)
{
ostringstream vname;
vname << Workers[w] << "." << Shifts[s];
x[w][s].set(GRB_DoubleAttr_UB, availability[w][s]);
x[w][s].set(GRB_CharAttr_VType, GRB_BINARY);
x[w][s].set(GRB_StringAttr_VarName, vname.str());
}
}
// Slack variables for each shift constraint so that the shifts can
// be satisfied
slacks = model.addVars(nShifts);
model.update();
for (int s = 0; s < nShifts; ++s)
{
ostringstream vname;
vname << Shifts[s] << "Slack";
slacks[s].set(GRB_StringAttr_VarName, vname.str());
}
// Variable to represent the total slack
GRBVar totSlack = model.addVar(0, GRB_INFINITY, 0, GRB_CONTINUOUS,
"totSlack");
// Variables to count the total shifts worked by each worker
totShifts = model.addVars(nWorkers);
model.update();
for (int w = 0; w < nWorkers; ++w)
{
ostringstream vname;
vname << Workers[w] << "TotShifts";
totShifts[w].set(GRB_StringAttr_VarName, vname.str());
}
// Update model to integrate new variables
model.update();
GRBLinExpr lhs;
// Constraint: assign exactly shiftRequirements[s] workers
// to each shift s
for (int s = 0; s < nShifts; ++s)
{
lhs = 0;
lhs += slacks[s];
for (int w = 0; w < nWorkers; ++w)
{
lhs += x[w][s];
}
model.addConstr(lhs == shiftRequirements[s], Shifts[s]);
}
// Constraint: set totSlack equal to the total slack
lhs = 0;
for (int s = 0; s < nShifts; ++s)
{
lhs += slacks[s];
}
model.addConstr(lhs == totSlack, "totSlack");
// Constraint: compute the total number of shifts for each worker
for (int w = 0; w < nWorkers; ++w) {
lhs = 0;
for (int s = 0; s < nShifts; ++s) {
lhs += x[w][s];
}
ostringstream vname;
vname << "totShifts" << Workers[w];
model.addConstr(lhs == totShifts[w], vname.str());
}
// Objective: minimize the total slack
GRBLinExpr obj = 0;
obj += totSlack;
model.setObjective(obj);
// Optimize
int status = solveAndPrint(model, totSlack, nWorkers, Workers, totShifts);
if (status != GRB_OPTIMAL)
{
return 1;
}
// Constrain the slack by setting its upper and lower bounds
totSlack.set(GRB_DoubleAttr_UB, totSlack.get(GRB_DoubleAttr_X));
totSlack.set(GRB_DoubleAttr_LB, totSlack.get(GRB_DoubleAttr_X));
// Variable to count the average number of shifts worked
GRBVar avgShifts =
model.addVar(0, GRB_INFINITY, 0, GRB_CONTINUOUS, "avgShifts");
// Variables to count the difference from average for each worker;
// note that these variables can take negative values.
diffShifts = model.addVars(nWorkers);
model.update();
for (int w = 0; w < nWorkers; ++w) {
ostringstream vname;
vname << Workers[w] << "Diff";
diffShifts[w].set(GRB_StringAttr_VarName, vname.str());
diffShifts[w].set(GRB_DoubleAttr_LB, -GRB_INFINITY);
}
// Update model to integrate new variables
model.update();
// Constraint: compute the average number of shifts worked
lhs = 0;
for (int w = 0; w < nWorkers; ++w) {
lhs += totShifts[w];
}
model.addConstr(lhs == nWorkers * avgShifts, "avgShifts");
// Constraint: compute the difference from the average number of shifts
for (int w = 0; w < nWorkers; ++w) {
lhs = 0;
lhs += totShifts[w];
lhs -= avgShifts;
ostringstream vname;
vname << Workers[w] << "Diff";
model.addConstr(lhs == diffShifts[w], vname.str());
}
// Objective: minimize the sum of the square of the difference from the
// average number of shifts worked
GRBQuadExpr qobj;
for (int w = 0; w < nWorkers; ++w) {
qobj += diffShifts[w] * diffShifts[w];
}
model.setObjective(qobj);
// Optimize
status = solveAndPrint(model, totSlack, nWorkers, Workers, totShifts);
if (status != GRB_OPTIMAL)
{
return 1;
}
}
catch (GRBException e)
{
cout << "Error code = " << e.getErrorCode() << endl;
cout << e.getMessage() << endl;
}
catch (...)
{
cout << "Exception during optimization" << endl;
}
delete[] c;
delete[] v;
for (int i = 0; i < xCt; ++i) {
delete[] x[i];
}
delete[] x;
delete[] slacks;
delete[] totShifts;
delete[] diffShifts;
delete env;
return 0;
}
// Start the optimization
SolverResult SolverGurobi::runOptimizer()
{
if (!getInitialized())
initialize();
try
{
// Create Gurobi environment and set parameters
GRBEnv env = GRBEnv();
env.set(GRB_IntParam_OutputFlag, 0);
GRBModel model = GRBModel(env);
// Get problem info
int numVars = constraints->getNumVariables();
int numConstraints = constraints->getNumConstraints();
// Get variables
auto variables = constraints->getVariables();
// Create array of model variables
GRBVar vars[numVars];
for (int i = 0; i < numVars; i++)
{
//vars[i] = model.addVar(0.0, 1.0, 0.0, GRB_BINARY);
// Set variable type
char type = GRB_CONTINUOUS;
if (variables.at(i)->getType() == VariableType::BINARY)
{
type = GRB_BINARY;
}
else if (variables.at(i)->getType() == VariableType::INTEGER)
{
type = GRB_INTEGER;
}
vars[i] = model.addVar(variables.at(i)->getLowerBound(),
variables.at(i)->getUpperBound(),
variables.at(i)->getCost(),
type);
}
// Integrate variables into model
model.update();
// Set starting points (does not help much...)
for (int i = 0; i < numVars; i++)
vars[i].set(GRB_DoubleAttr_Start, variables.at(i)->getValue());
/*
* Add constraints Ax <= b (or Ax = b)
* by evaluating gradient and build A matrix
*/
DenseVector x = DenseVector::Zero(numVars);
DenseVector dx = constraints->evalJacobian(x);
// Get constraint bounds
std::vector<double> clb;
std::vector<double> cub;
constraints->getConstraintBounds(clb,cub);
std::vector<int> rowGradient, colGradient;
constraints->structureJacobian(rowGradient, colGradient);
int nnzJacobian = constraints->getNumNonZerosJacobian();
// Add constraints one row at the time
for (int row = 0; row < numConstraints; row++)
{
// Build constraint
GRBLinExpr expr = 0;
// Loop through all non-zeros (inefficient)
for (int i = 0; i < nnzJacobian; i++)
{
if (rowGradient.at(i) == row)
{
int j = colGradient.at(i);
expr += dx(i)*vars[j];
}
}
// Add constraint to model
if (clb.at(row) == cub.at(row))
{
model.addConstr(expr, GRB_EQUAL, cub.at(row));
}
else
{
model.addConstr(expr, GRB_LESS_EQUAL, cub.at(row));
}
}
// More efficient method - avoids dense matrix
// std::vector<int> rows = {1,1,1,2,2,3,4,4,4,4,4,5};
// std::vector<int>::iterator start,stop;
// start = rows.begin();
// stop = start;
// while (start != rows.end())
// {
// while (stop != rows.end())
// {
// if (*stop == *start)
// ++stop;
// else
// break;
// }
// for (std::vector<int>::iterator it = start; it != stop; ++it)
// cout << *it << endl;
// start = stop;
// }
model.update();
assert(numVars == model.get(GRB_IntAttr_NumVars));
assert(numConstraints == model.get(GRB_IntAttr_NumConstrs));
// Optimize model
model.optimize();
// Check status
int optimstatus = model.get(GRB_IntAttr_Status);
if (optimstatus == GRB_INF_OR_UNBD)
{
model.getEnv().set(GRB_IntParam_Presolve, 0);
model.optimize();
optimstatus = model.get(GRB_IntAttr_Status);
}
// Create result object
SolverResult result(SolverStatus::ERROR, INF, std::vector<double>(numVars,0));
// Check Gurobi status
if (optimstatus == GRB_OPTIMAL)
{
result.status = SolverStatus::OPTIMAL;
// Get solution info
result.objectiveValue = model.get(GRB_DoubleAttr_ObjVal);
std::vector<double> optimalSolution;
for (int i = 0; i < numVars; i++)
{
optimalSolution.push_back(vars[i].get(GRB_DoubleAttr_X));
}
result.primalVariables = optimalSolution;
/*
* Reduced costs and constraint duals are
* only available for continuous models
*/
std::vector<double> reducedCosts;
std::vector<double> constraintDuals;
if (!model.get(GRB_IntAttr_IsMIP))
{
for (int i = 0; i < numVars; i++)
{
// Get reduced costs (related to range constraint duals)
reducedCosts.push_back(vars[i].get(GRB_DoubleAttr_RC));
}
for (int i = 0; i < numConstraints; i++)
{
GRBConstr c = model.getConstr(i);
double pi = c.get(GRB_DoubleAttr_Pi);
constraintDuals.push_back(pi);
}
}
result.lowerBoundDualVariables = reducedCosts;
result.upperBoundDualVariables = reducedCosts;
result.constraintDualVariables = constraintDuals;
return result;
}
else if (optimstatus == GRB_INFEASIBLE)
{
result.status = SolverStatus::INFEASIBLE;
result.objectiveValue = INF;
// compute and write out IIS
// model.computeIIS();
// model.write("problem.lp");
return result;
}
else if (optimstatus == GRB_UNBOUNDED)
{
result.status = SolverStatus::UNBOUNDED;
result.objectiveValue = -INF;
return result;
}
else
{
result.status = SolverStatus::ERROR;
result.objectiveValue = INF;
return result;
}
}
catch(GRBException e)
{
cout << "SolverGurobi: Error code = " << e.getErrorCode() << endl;
cout << e.getMessage() << endl;
return SolverResult(SolverStatus::ERROR, INF, std::vector<double>(constraints->getNumVariables(),0));
}
catch (...)
{
cout << "SolverGurobi: Error during optimization!" << endl;
return SolverResult(SolverStatus::ERROR, INF, std::vector<double>(constraints->getNumVariables(),0));
}
}
int main (int argc, char * argv[]) {
chrono :: steady_clock :: time_point tBegin = chrono :: steady_clock :: now();
string I ("0");
ulint timeLimit = 10;
if (argc >= 2) {
I = string (argv[1]);
}
if (argc >= 3) {
timeLimit = atoi(argv[2]);
}
ulint nComplete, k, t, n, m, root;
double d;
cin >> nComplete >> d >> k >> t >> n >> m >> root;
vector <ulint> penalty (nComplete); // vector with de penalties of each vectex
vector < list < pair <ulint, ulint> > > adj (nComplete); // adjacency lists for the graph
for (ulint v = 0; v < nComplete; v++) {
cin >> penalty[v];
}
vector <ulint> solutionV (nComplete, 0);
// reading solution vertices
for (ulint i = 0; i < n; i++) {
ulint v;
cin >> v;
solutionV[v] = 1;
}
vector < pair < pair <ulint, ulint> , ulint> > E (m); // vector of edges with the format ((u, v), w)
map < pair <ulint, ulint>, ulint> mE; // map an edge to its ID
vector < vector <ulint> > paths (m);
// reading graph
for (ulint e = 0; e < m; e++) {
ulint u, v, w, pathSize;
cin >> u >> v >> w >> pathSize;
adj[u].push_back(make_pair(v, w));
adj[v].push_back(make_pair(u, w));
E[e] = make_pair(make_pair(u, v), w);
mE[make_pair(u, v)] = e;
mE[make_pair(v, u)] = e;
paths[e] = vector <ulint> (pathSize);
for (ulint i = 0; i < pathSize; i++) {
cin >> paths[e][i];
}
}
try {
string N = itos(nComplete);
stringstream ssD;
ssD << fixed << setprecision(1) << d;
string D = ssD.str();
D.erase(remove(D.begin(), D.end(), '.'), D.end());
string K = itos(k);
string T = itos(t);
ifstream remainingTimeFile ("./output/N" + N + "D" + D + "K" + K + "T" + T + "I" + I + "/remainingTime.txt");
lint remainingTime = 0;
if (remainingTimeFile.is_open()) {
remainingTimeFile >> remainingTime;
}
if (remainingTime > 0) {
timeLimit += remainingTime;
}
GRBEnv env = GRBEnv();
env.set(GRB_IntParam_LazyConstraints, 1);
env.set(GRB_IntParam_LogToConsole, 0);
env.set(GRB_StringParam_LogFile, "./output/N" + N + "D" + D + "K" + K + "T" + T + "I" + I + "/log2.txt");
env.set(GRB_DoubleParam_TimeLimit, ((double) timeLimit));
GRBModel model = GRBModel(env);
model.getEnv().set(GRB_IntParam_LazyConstraints, 1);
model.getEnv().set(GRB_IntParam_LogToConsole, 0);
model.getEnv().set(GRB_StringParam_LogFile, "./output/N" + N + "D" + D + "K" + K + "T" + T + "I" + I + "/log2.txt");
model.getEnv().set(GRB_DoubleParam_TimeLimit, ((double) timeLimit));
vector <GRBVar> y (nComplete);
// ∀ v ∈ V
for (ulint v = 0; v < nComplete; v++) {
// y_v ∈ {0.0, 1.0}
y[v] = model.addVar(0.0, 1.0, 0.0, GRB_BINARY, "y_" + itos(v));
}
vector <GRBVar> x (m);
// ∀ e ∈ E
for (ulint e = 0; e < m; e++) {
ulint u, v;
u = E[e].first.first;
v = E[e].first.second;
// y_e ∈ {0.0, 1.0}
x[e] = model.addVar(0.0, 1.0, 0.0, GRB_BINARY, "x_" + itos(u) + "_" + itos(v));
}
model.update();
GRBLinExpr obj = 0.0;
// obj = ∑ ce * xe
for (ulint e = 0; e < m; e++) {
ulint w;
w = E[e].second;
obj += w * x[e];
}
// obj += ∑ πv * (1 - yv)
for (ulint v = 0; v < nComplete; v++) {
obj += penalty[v] * (1.0 - y[v]);
}
model.setObjective(obj, GRB_MINIMIZE);
// yu == 1
model.addConstr(y[root] == 1.0, "c_0");
// dominance
// ∀ v ∈ V
for (ulint v = 0; v < nComplete; v++) {
if (solutionV[v] == 1) {
GRBLinExpr constr = 0.0;
constr += y[v];
model.addConstr(constr == 1, "c_1_" + itos(v));
}
}
// each vertex must have exactly two edges adjacent to itself
// ∀ v ∈ V
for (ulint v = 0; v < nComplete; v++) {
// ∑ xe == 2 * yv , e ∈ δ({v})
GRBLinExpr constr = 0.0;
for (list < pair <ulint, ulint> > :: iterator it = adj[v].begin(); it != adj[v].end(); it++) {
ulint w = (*it).first; // destination
ulint e = mE[make_pair(v, w)];
constr += x[e];
}
model.addConstr(constr == 2.0 * y[v], "c_2_" + itos(v));
}
subtourelim cb = subtourelim(y, x, nComplete, m, E, mE, root);
model.setCallback(&cb);
model.optimize();
if (model.get(GRB_IntAttr_SolCount) > 0) {
ulint solutionCost = 0;
set <ulint> solutionVectices;
vector < pair <ulint, ulint> > solutionEdges;
solutionCost = round(model.get(GRB_DoubleAttr_ObjVal));
for (ulint v = 0; v < nComplete; v++) {
if (y[v].get(GRB_DoubleAttr_X) >= 0.5) {
solutionVectices.insert(v);
}
}
for (ulint e = 0; e < m; e++) {
if (x[e].get(GRB_DoubleAttr_X) >= 0.5) {
for (ulint i = 0; i < paths[e].size() - 1; i++) {
pair <ulint, ulint> edge;
if (paths[e][i] < paths[e][i + 1]) {
edge.first = paths[e][i];
edge.second = paths[e][i + 1];
} else {
edge.first = paths[e][i + 1];
edge.second = paths[e][i];
}
solutionEdges.push_back(edge);
}
}
}
cout << solutionVectices.size() << ' ' << solutionEdges.size() << ' ' << solutionCost << endl;
for (set <ulint> :: iterator it = solutionVectices.begin(); it != solutionVectices.end(); it++) {
ulint v = *it;
cout << v << endl;
}
for (vector < pair <ulint, ulint> > :: iterator it = solutionEdges.begin(); it != solutionEdges.end(); it++) {
pair <ulint, ulint> e = *it;
cout << e.first << " " << e.second << endl;
}
} else {
cout << "0 0 0" << endl;
}
// exporting model
model.write("./output/N" + N + "D" + D + "K" + K + "T" + T + "I" + I + "/model2.lp");
ofstream objValFile ("./output/N" + N + "D" + D + "K" + K + "T" + T + "I" + I + "/objVal2.txt", ofstream :: out);
objValFile << model.get(GRB_DoubleAttr_ObjVal);
objValFile.close();
ofstream gapFile ("./output/N" + N + "D" + D + "K" + K + "T" + T + "I" + I + "/gap2.txt", ofstream :: out);
gapFile << model.get(GRB_DoubleAttr_MIPGap);
gapFile.close();
chrono :: steady_clock :: time_point tEnd = chrono :: steady_clock :: now();
chrono :: nanoseconds elapsedTime = chrono :: duration_cast <chrono :: nanoseconds> (tEnd - tBegin);
ofstream elapsedTimeFile ("./output/N" + N + "D" + D + "K" + K + "T" + T + "I" + I + "/elapsedTime2.txt", ofstream :: out);
elapsedTimeFile << elapsedTime.count();
elapsedTimeFile.close();
} catch (GRBException e) {
int main(int argc, char *argv[]) {
// do we want to force integer solution?
// if false, we solve a linear program without the integrity constraint
bool integer_solution = false;
// if objective_min_N is true then we solve the problem which minimizes total
// number of vehicles in the simulation.
// Otherwise, we minimize number of rebalancing trips
bool objective_min_N = true;
GRBEnv* env = 0; //< gurobi env
GRBVar** rij = 0; // number of empty vehicles traveling between stations
GRBVar** vi = 0; // number of vehicles available at station i
// stations coordinates
std::vector<std::vector<double> > stations;
// distances or cost of traveling between the stations
// internal vector stores "to where" and external vector stores "from where"
std::vector<std::vector<double> > cost;
// origin counts, size of n_rebalancing_periods x n_stations
std::vector<std::vector<double> > origin_counts;
// destination counts, size of n_rebalancing_periods x n_stations
std::vector<std::vector<double> > dest_counts;
// counts of vehicles in transit to each station, size of n_rebalancing_periods x n_stations
std::vector<std::vector<double> > in_transit_counts;
// cost of one idle vehicle in the system, when objective minimizes # vehicles, then set cost to 1,
// when objective minimizes number of rebalancing vehicles then set cost to a huge number
// (to make sure that this is always more expensive that rebalancing as it adds more vehicles to the system)
double cost_of_veh = 1.0;
double rebalancing_cost = 1.0;
// what constraints do you want to take into account
// input and output files declaration
// simple_model
// bool simple_model = true;
// const string stationsFile = "/home/kasia/Dropbox/matlab/2015-09_FleetSizeEstimation/sampleFiles/stationsXY.txt";
// const string costMatrixFile = "/home/kasia/Dropbox/matlab/2015-09_FleetSizeEstimation/sampleFiles/costM3x3TT.txt";
// const string originCountsFile = "/home/kasia/Dropbox/matlab/2015-09_FleetSizeEstimation/sampleFiles/origCounts3x3TT.txt";
// const string destinationCountsFile = "/home/kasia/Dropbox/matlab/2015-09_FleetSizeEstimation/sampleFiles/destCounts3x3TT.txt";
// const string inTransitCountsFile = "/home/kasia/Dropbox/matlab/2015-09_FleetSizeEstimation/sampleFiles/inTransit3x3TT.txt";
// const string modelOutput = "rebalancing_formulation_simple.lp";
// const string solutionOutput = "rebalancing_solution_simple.sol";
// // simmobility files
// // ubuntu
bool simple_model = false;
const string stationsFile = "/home/kasia/Dropbox/matlab/2016-03-Demand_generation/facility_location/stations_ecbd34.txt";
const string costMatrixFile = "/home/kasia/Dropbox/matlab/2015-09_FleetSizeEstimation/RebTimeInSecs34Stations.txt";
const string originCountsFile = "/home/kasia/Dropbox/matlab/2015-09_FleetSizeEstimation/origCounts_rebEvery900_stations34.txt";
const string destinationCountsFile = "/home/kasia/Dropbox/matlab/2015-09_FleetSizeEstimation/destCounts_rebEvery900_stations34.txt";
const string inTransitCountsFile = "/home/kasia/Dropbox/matlab/2015-09_FleetSizeEstimation/inTransitReb900Stations34";
const string modelOutput = "formulation_rebalancing.lp";
const string solutionOutput = "rebalancing_solution.sol";
// mac
// stationsFile = "/Users/katarzyna/Dropbox/matlab/2016-03-Demand_generation/facility_location/stations_ecbd34.txt";
// costMatrixFile = "/Users/katarzyna/Dropbox/matlab/2015-09_FleetSizeEstimation/RebTime34Stations.txt";
// originCountsFile = "/Users/katarzyna/Dropbox/matlab/2015-09_FleetSizeEstimation/origCounts_rebEvery900_stations34.txt";
// destinationCountsFile = "/Users/katarzyna/Dropbox/matlab/2015-09_FleetSizeEstimation/destCounts_rebEvery900_stations34.txt";
//readFiles(stationsFile, stations);
readFiles(costMatrixFile, cost);
readFiles(originCountsFile, origin_counts);
readFiles(destinationCountsFile, dest_counts);
readFiles(inTransitCountsFile, in_transit_counts);
//round cost of traveling between stations to be a multiple of the rebalancing period
int reb_period = 1;
if(!simple_model) {
reb_period = 900; // in minutes, output from costOfRebalancing.m is in secs
}
int rounded_cost[cost.size()][cost.size()];
for (unsigned int i = 0; i < cost.size(); i++){
for (unsigned int j = 0; j < cost[0].size(); j++){
if (i != j) {
// cost in seconds from the beginning of the simulation
rounded_cost[i][j] = roundUp((int)cost[i][j], reb_period);
//std::cout << "roundUp((int)cost[" << i <<"][" << j << "] = " << rounded_cost[i][j] << std::endl;
} else {
rounded_cost[i][j] = 99999999; // 0
}
}
}
try {
// number of stations in the network
const int nStations = cost.size();
const int nStSquare = pow (nStations, 2);
//number of rebalancing periods = number of rows in the origin and destination counts, size of the first vector
const int nRebPeriods = origin_counts.size();
/***********************************************************************************
* Station matrix
***********************************************************************************/
// station matrix to access the elements of cost/rebalancing vectors
// matrix form stores the indices of the cost vector i.e., for 3 stations [[0,1,2],[3,4,5],[6,7,8]]
int stationMatrix [nStations][nStations];
int indxCounter = 0;
for(int i = 0; i < nStations; ++i) {
for (int k = 0; k < nStations; ++k) {
stationMatrix[i][k] = indxCounter;
//std::cout << "stationMatrix["<< i<< "][" << k << "] = " << indxCounter << endl;
indxCounter++;
}
}
/***********************************************************************************
* Model
***********************************************************************************/
env = new GRBEnv();
GRBModel model = GRBModel(*env);
model.set(GRB_StringAttr_ModelName, "rebalancing");
/***********************************************************************************
* Decision variables
***********************************************************************************/
int station;
int time_;
div_t divresult;
// Number of vehicles available (idle) at each period of time and each station
vi = new GRBVar* [nRebPeriods];
// Number of empty vehicles traveling between stations
rij = new GRBVar* [nRebPeriods];
for ( time_ = 0; time_ < nRebPeriods; ++time_) {
if (integer_solution) {
vi[time_] = model.addVars(nStations, GRB_INTEGER);
rij[time_] = model.addVars(nStSquare, GRB_INTEGER);
} else {
vi[time_] = model.addVars(nStations);
rij[time_] = model.addVars(nStSquare);
}
model.update();
}
// Objective: minimize number of rebalancing trips
if (!objective_min_N) {
for ( time_ = 0; time_ < nRebPeriods; ++time_) {
for (station = 0; station < nStations; ++station) {
ostringstream cname;
cname << "v_ti," << time_ << "," << station << ",0";
// not minimized in the objective
vi[time_][station].set(GRB_DoubleAttr_Obj, 0.0);
vi[time_][station].set(GRB_StringAttr_VarName, cname.str());
}
}
model.update();
for ( time_ = 0; time_ < nRebPeriods; ++time_) {
for(int depSt = 0; depSt < nStations; ++depSt){
// std::cout << "departure station: " << depSt << std::endl;
for (int arrSt = 0; arrSt < nStations; ++arrSt) {
int idx = stationMatrix[depSt][arrSt];
ostringstream vname;
vname << "r_tij," << time_ << "," << depSt << ","<< arrSt;
//std::cout << "nEmptyVhsTime." << time_ << "." << depSt << "."<< arrSt << "."<< idx << std::endl;
if (depSt == arrSt) {
// origin == destination
// this variable should not exist because we do not send vehicles within the same station
rij[time_][idx].set(GRB_DoubleAttr_Obj, 0.0);
rij[time_][idx].set(GRB_StringAttr_VarName, vname.str());
continue;
}
// std::cout << "nEmptyVhsTime." << time_ << ".indx." << station << ".from."<< divresult.quot << ".to." << divresult.rem << std:: endl;
rij[time_][idx].set(GRB_DoubleAttr_Obj, rebalancing_cost * rounded_cost[depSt][arrSt]); // rebalancing_cost
rij[time_][idx].set(GRB_StringAttr_VarName, vname.str());
}
}
}
} else { // Objective: minimize the total number of vehicles at time_ == 0
// integer constraint relaxed
ostringstream cname;
ostringstream vname;
for ( time_ = 0; time_ < nRebPeriods; ++time_) {
for(int depSt = 0; depSt < nStations; ++depSt){
cname.str("");
cname.clear();
cname << "v_ti," << time_ <<"," << depSt << ",0";
if (time_ != 0) {
cost_of_veh = 0.0;
} else {
cost_of_veh = 1.0;
}
vi[time_][depSt].set(GRB_DoubleAttr_Obj, cost_of_veh);
vi[time_][depSt].set(GRB_StringAttr_VarName, cname.str());
model.update();
for (int arrSt = 0; arrSt < nStations; ++arrSt) {
vname.str("");
vname.clear();
vname << "r_tij," << time_ << "," << depSt << ","<< arrSt;
// std::cout << "Adding variable: r_tij," << time_ << "," << depSt << ","<< arrSt << " = \n" << std::flush;
int idx = stationMatrix[depSt][arrSt];
if (depSt == arrSt) {
rij[time_][idx].set(GRB_DoubleAttr_Obj, 0.0);
rij[time_][idx].set(GRB_StringAttr_VarName, vname.str());
// std::cout << "vname = " << vname.str() << "\n" << std::flush;
// std::cout << "vname after clear= " << vname.str() << "\n" << std::flush;
// std::cout << "Added variable: r_tij," << time_ << "," << depSt << ","<< arrSt << " = 0.0\n" << std::flush;
continue;
}
if (time_ != 0) {
rebalancing_cost = 0.0;
} else {
rebalancing_cost = 1.0;
}
int travel_time = (int) (rounded_cost[arrSt][depSt]/reb_period);
// divresult.rem is start time of the arriving trips
divresult = div (nRebPeriods + time_ - travel_time, nRebPeriods);
// to prevent overwriting if there is sth already assigned to this variable
if (rij[divresult.rem][idx].get(GRB_DoubleAttr_Obj) > 0) {
if (travel_time > 1) {
for (int k = 1; k < travel_time; ++k) {
divresult = div (nRebPeriods + time_ - travel_time + k, nRebPeriods);
// to prevent overwriting if there is sth already assigned to this variable
if (rij[divresult.rem][idx].get(GRB_DoubleAttr_Obj) > 0) {
continue;
}
rij[divresult.rem][idx].set(GRB_DoubleAttr_Obj, rebalancing_cost);
vname.str("");
vname.clear();
vname << "r_tij," << divresult.rem << "," << depSt << ","<< arrSt;
rij[divresult.rem][idx].set(GRB_StringAttr_VarName, vname.str());
//std::cout << "Added variable: r_tij," << divresult.rem << "," << depSt << ","<< arrSt << " = " << rebalancing_cost << "\n" << std::flush;
}
}
continue;
}
rij[divresult.rem][idx].set(GRB_DoubleAttr_Obj, rebalancing_cost);
vname.str("");
vname.clear();
vname << "r_tij," << divresult.rem << "," << depSt << ","<< arrSt;
rij[divresult.rem][idx].set(GRB_StringAttr_VarName, vname.str());
//std::cout << "Added variable: r_tij," << divresult.rem << "," << depSt << ","<< arrSt << " = " << rebalancing_cost << "\n" << std::flush;
if (travel_time > 1) {
for (int k = 1; k < travel_time; ++k) {
divresult = div (nRebPeriods + time_ - travel_time + k, nRebPeriods);
// to prevent overwriting if there is sth already assigned to this variable
if (rij[divresult.rem][idx].get(GRB_DoubleAttr_Obj) > 0) {
continue;
}
rij[divresult.rem][idx].set(GRB_DoubleAttr_Obj, rebalancing_cost);
vname.str("");
vname.clear();
vname << "r_tij," << divresult.rem << "," << depSt << ","<< arrSt;
rij[divresult.rem][idx].set(GRB_StringAttr_VarName, vname.str());
//std::cout << "Added variable: r_tij," << divresult.rem << "," << depSt << ","<< arrSt << " = " << rebalancing_cost << "\n" << std::flush;
}
}
}
}
}
}
model.update();
/***********************************************************************************
* Objective function
***********************************************************************************/
// The objective is to minimize the number of vehicles traveling in the network (and cost associated with it)
// Optimization sense: The default +1.0 value indicates that the objective is to minimize the
// objective. Setting this attribute to -1 changes the sense to maximization
model.set(GRB_IntAttr_ModelSense, 1);
model.update();
/***********************************************************************************
* Constraint 1: The number of vehicles must be sufficient to serve the demand
***********************************************************************************/
// This constraint is set to ensure that the number of vehicles available at each station i
// is sufficient to serve the demand within this station.
// If there is not enough vehicles, then we do rebalancing to make sure we can serve the trips
for ( time_ = 0; time_ < nRebPeriods; ++time_) {
// Current demand
int booking_requests[nStations];
for (int i = 0; i < nStations; ++i) {
// booking_requests = departing_vehicles - arriving_vehicles (how many more vehicles do we need)
booking_requests[i] = origin_counts[time_][i] - dest_counts[time_][i];
// std::cout << origin_counts[time_][i] << " - " << dest_counts[time_][i] << " = " << dem_curr[i] << std::endl;
}
GRBLinExpr reb_dep = 0;
GRBLinExpr reb_arr = 0;
for(int depSt = 0; depSt < nStations; ++depSt){
// std::cout << "departure station: " << depSt << std::endl;
for (int arrSt = 0; arrSt < nStations; ++arrSt) {
if (depSt != arrSt) {
int idx = stationMatrix[depSt][arrSt];
int idx2 = stationMatrix[arrSt][depSt];
reb_dep += rij[time_][idx];
// std::cout << "rounded_cost = " << (int)rounded_cost[depSt][arrSt]/reb_period -1 << std::endl;
// cost is expressed in the number of reb periods, i.e., 1,2,3,...nRebPeriods
int travel_cost = (int) (rounded_cost[depSt][arrSt]/reb_period); // maybe ceil would be better than just int
// ceil version of the above line
// int travel_cost = (int) ceil((rounded_cost[depSt][arrSt]/reb_period));
if (travel_cost > time_) {
int dep_time = nRebPeriods + time_ - travel_cost;
reb_arr += rij[dep_time][idx2];
} else {
int dep_time = time_ - travel_cost;
reb_arr += rij[dep_time][idx2];
}
}
}
ostringstream cname;
cname << "demand_ti" << time_ << "." << depSt;
model.addConstr(vi[time_][depSt] + reb_arr - reb_dep >= booking_requests[depSt], cname.str());
//std::cout << "Constraint 1 demand: " << time_ << "." << depSt << std::endl;
reb_arr.clear();
reb_dep.clear();
}
}
model.update();
std::cout << "Constraint set 1 added." << std::endl;
/***********************************************************************************
* Constraint 2: Constant number of vehicles over time
***********************************************************************************/
// This constraint is set to ensure that the total number of vehicles in the system does not change
// over time
// the total number of vehicles in the system is equal to the sum of the vehicles owned by each station
// vehicles owned by station i: available_veh + cust_arriving + cust_in_transit_to_station + empty_arr + empty_in_transit
int cust_balance_at[nStations];
int cust_vhs_at_t[time_];
for ( time_ = 0; time_ < nRebPeriods; ++time_) {
cust_vhs_at_t[time_] = 0;
for (int i = 0; i < nStations; ++i) {
// vhs_owned is the number of vehicles owned by station i
// this number does not account for rebalancing vehicles
// vhs_owned = arriving_costomers + in_transit_to_station
cust_balance_at[i] = dest_counts[time_][i] + in_transit_counts[time_][i];
// std::cout << "vhs_owned[" << i << "] = " << vhs_owned[i] << std::endl;
cust_vhs_at_t[time_] += cust_balance_at[i];
}
if (time_ == 0) { // one print function to validate the above loop
std::cout << "cust_vhs_at_t[" << time_ << "] = " << cust_vhs_at_t[time_] << std::endl;
}
}
GRBLinExpr idle_veh = 0; // Gurobi Linear Expression, total number of idle vehicles now
GRBLinExpr reb_arr = 0; // Gurobi Linear Expression, total number of rebalancing vehicles arriving and in transit to station i
GRBLinExpr idle_veh_prev = 0; // Gurobi Linear Expression, total number of idle vehicles at previous interval
GRBLinExpr reb_arr_prev = 0; // Gurobi Linear Expression, total number of rebalancing vehicles arriving and in transit to station i at previous time
for ( time_ = 0; time_ < nRebPeriods; ++time_) {
std::cout << "time_ = " << time_ << std::endl;
// adding variables at current time t
for(int i = 0; i < nStations; ++i) {
// idle vehicles at station i at time t
idle_veh += vi[time_][i];
for(int j = 0; j < nStations; ++j) {
if (i != j) {
// travel_time is expressed in the number of reb periods, i.e., 1,2,3,...nRebPeriods
// travel_time for trips arriving to i from j
int travel_time = (int) (rounded_cost[j][i]/reb_period);
// index of vehicles arriving at i from j (departed from j to i travel_time ago)
int idx_arr = stationMatrix[j][i];
// divresult.rem is start time of the arriving trips
divresult = div (nRebPeriods + time_ - travel_time, nRebPeriods);
// std::cout << "Current time = " << time_ << ", travel_time = " << travel_time << ", divresult.rem = " << divresult.rem << ", idx_arr = "<< idx_arr << std::endl;
reb_arr += rij[divresult.rem][idx_arr];
// empty trips in transit (not arriving yet)
// if tt > 1 -> all trips started after divresult.rem and before time_
if (travel_time > 1) {
for (int k = 1; k < travel_time; ++k) {
divresult = div (nRebPeriods + time_ - travel_time + k, nRebPeriods);
// std::cout << "if travel_time > 1 => " << time_ << ", travel_time = " << travel_time << ", divresult.rem = " << divresult.rem << ", idx_arr = "<< idx_arr << std::endl;
reb_arr += rij[divresult.rem][idx_arr];
}
}
}
}
}
// adding variables at time (t-1)
for(int i = 0; i < nStations; ++i) {
// idle vehicles at station i at time (t - 1)
divresult = div (nRebPeriods + time_ - 1, nRebPeriods);
idle_veh_prev += vi[divresult.rem][i];
for(int j = 0; j < nStations; ++j) {
if (i != j) {
// travel_time is expressed in the number of reb periods, i.e., 1,2,3,...nRebPeriods
// travel_time for trips arriving to i from j
int travel_time = (int) (rounded_cost[j][i]/reb_period);
// index of vehicles arriving at i from j (departed from j to i travel_time ago)
int idx_arr = stationMatrix[j][i];
// divresult.rem is start time of the arriving trips
divresult = div (nRebPeriods + time_ - 1 - travel_time, nRebPeriods);
// std::cout << "Previous time = " << nRebPeriods + time_ - 1 << ", travel_time = " << travel_time << ", divresult.rem = " << divresult.rem << ", idx_arr = "<< idx_arr << std::endl;
reb_arr_prev += rij[divresult.rem][idx_arr];
// empty trips in transit (not arriving yet)
// if tt > 1 -> all trips started after divresult.rem and before time_
if (travel_time > 1) {
for (int k = 1; k < travel_time; ++k) {
divresult = div (nRebPeriods + time_ - 1 - travel_time + k, nRebPeriods);
reb_arr_prev += rij[divresult.rem][idx_arr];
}
}
}
}
}
// add constraints
ostringstream cname;
cname << "supply_t" << time_ ;
// total number of vehicles at time t == total number of vehicles at time (t-1)
divresult = div (nRebPeriods + time_ - 1, nRebPeriods);
model.addConstr(idle_veh + reb_arr + cust_vhs_at_t[time_] == idle_veh_prev + reb_arr_prev + cust_vhs_at_t[divresult.rem], cname.str());
model.update();
idle_veh.clear();
idle_veh_prev.clear();
reb_arr.clear();
reb_arr_prev.clear();
} // end constraints loop: for ( time_ = 0; time_ < nRebPeriods; ++time_)
std::cout << "Constraint set 2 added." << std::endl;
/***********************************************************************************
* Constraint 3: Flow conservation at station i
***********************************************************************************/
// This constraint is set to ensure that the number of vehicles available at station i at time t
// is equal to the number of vehicles available at this station in previous time interval
// plus whatever has arrived minus whatever has departed
for ( time_ = 0; time_ < nRebPeriods; ++time_) {
// Current demand
int dem_curr[nStations];
for (int i = 0; i < nStations; ++i) {
// current demand = arriving_vehicles - departing_vehicles
dem_curr[i] = dest_counts[time_][i] - origin_counts[time_][i];
// std::cout << origin_counts[time_][i] << " - " << dest_counts[time_][i] << " = " << dem_curr[i] << std::endl;
}
GRBLinExpr reb_dep = 0;
GRBLinExpr reb_arr = 0;
for(int depSt = 0; depSt < nStations; ++depSt){
// std::cout << "departure station: " << depSt << std::endl;
for (int arrSt = 0; arrSt < nStations; ++arrSt) {
if (depSt != arrSt) {
int idx = stationMatrix[depSt][arrSt];
int idx2 = stationMatrix[arrSt][depSt];
reb_dep += rij[time_][idx];
// std::cout << "rounded_cost = " << (int)rounded_cost[depSt][arrSt]/reb_period -1 << std::endl;
int travel_cost = (int) (rounded_cost[depSt][arrSt]/reb_period);
if (travel_cost > time_) {
int dep_time = nRebPeriods + time_ - travel_cost;
reb_arr += rij[dep_time][idx2];
} else {
int dep_time = time_ - travel_cost;
reb_arr += rij[dep_time][idx2];
}
}
}
ostringstream cname;
cname << "flow_ti" << time_ << "." << depSt;
if (time_ != nRebPeriods - 1) {
model.addConstr(vi[time_ + 1][depSt] ==
vi[time_][depSt] + reb_arr - reb_dep + dem_curr[depSt], cname.str());
} else {
// if we are in the last interval then we compare against the first interval of the next day
// here: vi(t=0) == vi(t=Tp)
model.addConstr(vi[0][depSt] ==
vi[time_][depSt] + reb_arr - reb_dep + dem_curr[depSt], cname.str());
}
//std::cout << "Constraint 1 vi @: " << time_ << "." << depSt << std::endl;
reb_arr.clear();
reb_dep.clear();
}
}
model.update();
std::cout << "Constraint set 3 added." << std::endl;
/***********************************************************************************
* Solve the problem and print solution to the console and to the file
***********************************************************************************/
model.optimize();
model.write(modelOutput);
int total_demand = 0;
int dem_curr[nStations];
for (int i = 0; i < nStations; ++i) {
dem_curr[i] = dest_counts[0][i] + in_transit_counts[0][i];
total_demand += dem_curr[i];
}
double numOfVeh = model.get(GRB_DoubleAttr_ObjVal) + total_demand; //plus rebalancing counts
cout << "\nTOTAL NUMBER OF VEHICLES: " << numOfVeh << std::endl;
cout << "\nTOTAL NUMBER OF AVAILABLE VEH AT TIME 0: " << model.get(GRB_DoubleAttr_ObjVal) << std::endl;
// cout << "SOLUTION:" << endl;
//
// for (time_ = 0; time_ < nRebPeriods; ++time_){
//
// for(int depSt = 0; depSt < nStations; ++depSt){
// for (int arrSt = 0; arrSt < nStations; ++arrSt) {
// if(depSt != arrSt) {
// int idx = stationMatrix[depSt][arrSt];
// cout << "At time " << time_ << ", rebalancing from station " << depSt <<
// " to station " << arrSt << " send " <<
// empty_veh[time_][idx].get(GRB_DoubleAttr_X) << " vehicles." << std::endl;
// }
// }
// }
// }
//
// for (time_ = 0; time_ < nRebPeriods; ++time_){
// for(int arrSt = 0; arrSt < nStations; ++arrSt){
// cout << "At time " << time_ << " at station " << arrSt <<
// " number of available vehicles: " << vhs_st_i[time_][arrSt].get(GRB_DoubleAttr_X) << std::endl;
// }
// cout << "At time " << time_ << " number of vehicles in transit: "
// << in_transit[time_].get(GRB_DoubleAttr_X) << std::endl;
// }
model.write(solutionOutput);
} catch(GRBException e) {
cout << "Error code = " << e.getErrorCode() << std::endl;
cout << e.getMessage() << std::endl;
} catch(...) {
cout << "Exception during optimization" << std::endl;
}
return 0;
}
int
main(int argc,
char *argv[])
{
GRBEnv* env = 0;
GRBVar* nutrition = 0;
GRBVar* buy = 0;
try
{
// Nutrition guidelines, based on
// USDA Dietary Guidelines for Americans, 2005
// http://www.health.gov/DietaryGuidelines/dga2005/
const int nCategories = 4;
string Categories[] = { "calories", "protein", "fat", "sodium" };
double minNutrition[] = { 1800, 91, 0, 0 };
double maxNutrition[] = { 2200, GRB_INFINITY, 65, 1779 };
// Set of foods
const int nFoods = 9;
string Foods[] = { "hamburger", "chicken", "hot dog", "fries", "macaroni", "pizza", "salad", "milk", "ice cream" };
double cost[] = { 2.49, 2.89, 1.50, 1.89, 2.09, 1.99, 2.49, 0.89, 1.59 };
// Nutrition values for the foods
double nutritionValues[][nCategories] = {
{ 410, 24, 26, 730 }, // hamburger
{ 420, 32, 10, 1190 }, // chicken
{ 560, 20, 32, 1800 }, // hot dog
{ 380, 4, 19, 270 }, // fries
{ 320, 12, 10, 930 }, // macaroni
{ 320, 15, 12, 820 }, // pizza
{ 320, 31, 12, 1230 }, // salad
{ 100, 8, 2.5, 125 }, // milk
{ 330, 8, 10, 180 } // ice cream
};
// Model
env = new GRBEnv();
GRBModel model = GRBModel(*env);
model.set(GRB_StringAttr_ModelName, "diet");
// Create decision variables for the nutrition information,
// which we limit via bounds
nutrition = model.addVars(minNutrition, maxNutrition, 0, 0, Categories, nCategories);
// Create decision variables for the foods to buy
buy = model.addVars(0, 0, cost, 0, Foods, nFoods);
// The objective is to minimize the costs
model.set(GRB_IntAttr_ModelSense, 1);
// Update model to integrate new variables
model.update();
// Nutrition constraints
for (int i = 0; i < nCategories; ++i)
{
GRBLinExpr ntot = 0;
for (int j = 0; j < nFoods; ++j)
{
ntot += nutritionValues[j][i] * buy[j];
}
model.addConstr(ntot == nutrition[i], Categories[i]);
}
// Solve
model.optimize();
printSolution(model, nCategories, nFoods, buy, nutrition);
// Add constraint
cout << "\nAdding constraint: at most 6 servings of dairy" << endl;
model.addConstr(buy[7] + buy[8] <= 6.0, "limit_dairy");
// Solve
model.optimize();
printSolution(model, nCategories, nFoods, buy, nutrition);
}
catch (GRBException e)
{
cout << "Error code = " << e.getErrorCode() << endl;
cout << e.getMessage() << endl;
}
catch (...)
{
cout << "Exception during optimization" << endl;
}
delete[] nutrition;
delete[] buy;
delete env;
return 0;
}
int main(int argc, char *argv[])
{
int time_limit;
char name[1000];
ListGraph g;
EdgeWeight lpvar(g);
EdgeWeight weight(g);
NodeName vname(g);
ListGraph::NodeMap<double> posx(g),posy(g);
string filename;
int seed=1;
// uncomment one of these lines to change default pdf reader, or insert new one
//set_pdfreader("open"); // pdf reader for Mac OS X
//set_pdfreader("xpdf"); // pdf reader for Linux
//set_pdfreader("evince"); // pdf reader for Linux
srand48(seed);
time_limit = 3600; // solution must be obtained within time_limit seconds
if (argc!=2) {cout<< endl << "Usage: "<< argv[0]<<" <graph_filename>"<<endl << endl <<
"Example: " << argv[0] << " gr_berlin52" << endl <<
" " << argv[0] << " gr_att48" << endl << endl; exit(0);}
else if (!FileExists(argv[1])) {cout<<"File "<<argv[1]<<" does not exist."<<endl; exit(0);}
filename = argv[1];
// Read the graph
if (!ReadListGraph(filename,g,vname,weight,posx,posy))
{cout<<"Error reading graph file "<<argv[1]<<"."<<endl;exit(0);}
TSP_Data tsp(g,vname,posx,posy,weight);
ListGraph::EdgeMap<GRBVar> x(g);
GRBEnv env = GRBEnv();
GRBModel model = GRBModel(env);
#if GUROBI_NEWVERSION
model.getEnv().set(GRB_IntParam_LazyConstraints, 1);
model.getEnv().set(GRB_IntParam_Seed, seed);
#else
model.getEnv().set(GRB_IntParam_DualReductions, 0); // Dual reductions must be disabled when using lazy constraints
#endif
model.set(GRB_StringAttr_ModelName, "Emparelhamento perfeito with GUROBI");
// name to the problem
model.set(GRB_IntAttr_ModelSense, GRB_MAXIMIZE); // is a minimization problem
// Add one binary variable for each edge and also sets its cost in
//the objective function
for (ListGraph::EdgeIt e(g); e!=INVALID; ++e) {
sprintf(name,"x_%s_%s",vname[g.u(e)].c_str(),vname[g.v(e)].c_str());
x[e] = model.addVar(0.0, 1.0, weight[e],GRB_BINARY,name);
}
model.update(); // run update to use model inserted variables
// Add degree constraint for each node (sum of solution edges incident to a node is 2)
for (ListGraph::NodeIt v(g); v!=INVALID; ++v) {
GRBLinExpr expr;
for (ListGraph::IncEdgeIt e(g,v); e!=INVALID; ++e) expr += x[e];
//aqui model.addConstr(expr == 2 ); what? ignorou!
model.addConstr(expr == 1);
}
try {
model.update(); // Process any pending model modifications.
if (time_limit >= 0) model.getEnv().set(GRB_DoubleParam_TimeLimit,time_limit);
model.update(); // Process any pending model modifications.
model.write("model.lp");
system("cat model.lp");
model.optimize();
double soma=0.0;
int i = 0;
for (ListGraph::EdgeIt e(g); e!=INVALID; ++e) {
lpvar[e] = x[e].get(GRB_DoubleAttr_X);
if (lpvar[e] > 1-BC_EPS ) {
soma += weight[e];
cout << "Achei, x("<< vname[g.u(e)] << " , " << vname[g.v(e)] << ") = " << lpvar[e] <<"\n";
tsp.BestCircuit[i] = g.u(e);
tsp.BestCircuit[i+1] = g.v(e);
i = i+2;
}
}
cout << "Solution cost = "<< soma << endl;
ViewTspCircuit(tsp);
}catch (...) {
if (tsp.BestCircuitValue < DBL_MAX) {
cout << "Heuristic obtained optimum solution" << endl;
ViewTspCircuit(tsp);
return 0;
}else {
cout << "Graph is infeasible" << endl;
return 1;
}
}
}
