void seleciona_propagandas(int n, double C, double V[N], double P[N], double w[N][N], int S[N], double *UpperBound, long maxtime) {
GRBVar y[N][N];
GRBVar x[N];
int seed=0;
GRBEnv env = GRBEnv();
GRBModel model = GRBModel(env);
model.getEnv().set(GRB_IntParam_Seed, seed);
model.set(GRB_StringAttr_ModelName, "Alocacao Propaganda"); // prob. name
model.set(GRB_IntAttr_ModelSense, GRB_MAXIMIZE); // is a minimization problem
for(int i = 0; i < n; i++) {
char name[100];
sprintf(name,"x_%d",i);
x[i] = model.addVar(0.0, 1.0, V[i],GRB_BINARY, name);
}
for( int i = 0; i < n; i++) {
for(int j = i+1; j < n; j++) {
char name[100];
sprintf(name,"y_%d_%d",i, j);
y[i][j] = model.addVar(0.0, 1.0, w[i][j], GRB_BINARY, name);
}
}
model.update();
GRBLinExpr expr;
for(int i = 0; i < n; i++) {
expr += (x[i] * P[i]);
}
model.addConstr(expr <= C);
model.update();
for(int i = 0; i < n; i++) {
for(int j =i+1; j < n; j++) {
model.addConstr(y[i][j] <= x[i]);
model.addConstr(y[i][j] <= x[j]);
model.addConstr(x[i] + x[j] <= (y[i][j] + 1));
}
}
model.update();
model.write("model.lp");
model.optimize();
*UpperBound = model.get(GRB_DoubleAttr_ObjVal);
}
int monitoramento_em_grafo_bipartido( ListGraph &g, NodeName &vname, ListGraph::NodeMap<double> &custo, ListGraph::NodeMap<int> &solucao)
{
int seed=0;
GRBEnv env = GRBEnv();
GRBModel model = GRBModel(env);
model.getEnv().set(GRB_IntParam_Seed, seed);
model.set(GRB_StringAttr_ModelName, "Monitoramento em Grafo Bipartido"); // prob. name
model.set(GRB_IntAttr_ModelSense, GRB_MINIMIZE); // is a minimization problem
// ------------------------------------------------------
// Construa o modelo daqui para baixo
// ------------------------------------------------------
// Exemplos de como voce pode declarar variaveis indexadas nos vertices ou nas arestas.
// Nao necessariamente voce precisa dos dois tipos
// ListGraph::NodeMap<GRBVar> x(g); // variables for each node
// ListGraph::EdgeMap<GRBVar> y(g); // variables for each edge
ListGraph::NodeMap<GRBVar> x(g); // variables for each node
ListGraph::EdgeMap<GRBVar> y(g); // variables for each edge
int name = 0;
char namme[100];
for(ListGraph::NodeIt v(g); v != INVALID; ++v) {
sprintf(namme,"PC_%s",vname[v].c_str());
x[v] = model.addVar(0.0, 1.0, custo[v],GRB_CONTINUOUS,namme); }
model.update();
try {
for(ListGraph::EdgeIt e(g); e != INVALID; ++e) {
//Para cada aresta, um dos lados e 1
GRBLinExpr expr;
expr += x[g.u(e)];
expr += x[g.v(e)];
model.addConstr(expr >= 1);
}
model.update();
// ------------------------------------------------------
// Construa o modelo daqui para cima
// ------------------------------------------------------
//model.write("model.lp"); system("cat model.lp");
model.optimize();
for (ListGraph::NodeIt v(g); v!=INVALID; ++v) {
if (x[v].get(GRB_DoubleAttr_X)>1-EPS) solucao[v] = 1;
else solucao[v] = 0;
//solucao[v] = 1;
}
return(1);
} catch (...) {cout << "Error during callback..." << endl; return(0);}
}
void solve(const Instance &inst, bool print_inst = false, bool pyout = false) {
Arcflow afg(inst);
char vtype = inst.vtype;
GRBEnv* env = new GRBEnv();
GRBModel model = GRBModel(*env);
model.set(GRB_StringAttr_ModelName, "flow");
model.getEnv().set(GRB_IntParam_OutputFlag, 1);
model.getEnv().set(GRB_IntParam_Threads, 1);
model.getEnv().set(GRB_IntParam_Presolve, 1);
// model.getEnv().set(GRB_IntParam_Method, 0);
model.getEnv().set(GRB_IntParam_Method, 2);
model.getEnv().set(GRB_IntParam_MIPFocus, 1);
// model.getEnv().set(GRB_IntParam_RINS, 1);
model.getEnv().set(GRB_DoubleParam_Heuristics, 1);
model.getEnv().set(GRB_DoubleParam_MIPGap, 0);
model.getEnv().set(GRB_DoubleParam_MIPGapAbs, 1-1e-5);
// model.getEnv().set(GRB_DoubleParam_ImproveStartTime, 60);
// model.getEnv().set(GRB_DoubleParam_ImproveStartGap, 1);
vector<Arc> As(afg.A);
sort(all(As));
map<Arc, GRBVar> va;
int lastv = afg.Ts[0]-1;
for (int i = 0; i < inst.nbtypes; i++) {
lastv = min(lastv, afg.Ts[i]-1);
}
for (int i = 0; i < 3; i++) {
for (const Arc &a : As) {
if (i == 1 && a.u != afg.S) {
continue;
}
if (i == 2 && a.v <= lastv) {
continue;
}
if (i == 0 && (a.u == afg.S || a.v > lastv)) {
continue;
}
if (a.label == afg.LOSS || inst.relax_domains) {
va[a] = model.addVar(
0.0, inst.n, 0, vtype);
} else {
va[a] = model.addVar(
0.0, inst.items[a.label].demand, 0, vtype);
}
}
}
model.update();
for (int i = 0; i < inst.nbtypes; i++) {
GRBVar &feedback = va[Arc(afg.Ts[i], afg.S, afg.LOSS)];
feedback.set(GRB_DoubleAttr_Obj, inst.Cs[i]);
if (inst.Qs[i] >= 0) {
feedback.set(GRB_DoubleAttr_UB, inst.Qs[i]);
}
}
vector<vector<Arc>> Al(inst.nsizes);
vector<vector<Arc>> in(afg.NV);
vector<vector<Arc>> out(afg.NV);
for (const Arc &a : As) {
if (a.label != afg.LOSS) {
Al[a.label].push_back(a);
}
out[a.u].push_back(a);
in[a.v].push_back(a);
}
for (int i = 0; i < inst.m; i++) {
GRBLinExpr lin = 0;
for (int it = 0; it < inst.nsizes; it++) {
if (inst.items[it].type == i) {
for (const Arc &a : Al[it]) {
lin += va[a];
}
}
}
if (inst.ctypes[i] == '>' || inst.relax_domains) {
model.addConstr(lin >= inst.demands[i]);
} else {
model.addConstr(lin == inst.demands[i]);
}
}
for (int u = 0; u < afg.NV; u++) {
GRBLinExpr lin = 0;
for (const Arc &a : in[u]) {
lin += va[a];
}
for (const Arc &a : out[u]) {
lin -= va[a];
}
model.addConstr(lin == 0);
}
Al.clear();
in.clear();
out.clear();
double pre = TIMEDIF(afg.tstart);
model.optimize();
printf("Preprocessing time: %.2f seconds\n", pre);
double tg = model.get(GRB_DoubleAttr_Runtime);
printf("Gurobi run time: %.2f seconds\n", tg);
printf("Total run time: %.2f seconds\n", tg+pre);
if (inst.vtype == 'I') {
map<Arc, int> flow;
for (const auto &a : va) {
double x = a.second.get(GRB_DoubleAttr_X);
int rx = static_cast<int>(round(x));
assert(x - rx <= EPS);
if (rx > 0) {
int u = a.first.u;
int v = a.first.v;
int lbl = a.first.label;
Arc a(u, v, lbl);
flow[a] = rx;
}
}
ArcflowSol solution(inst, flow, afg.S, afg.Ts, afg.LOSS, true);
solution.print_solution(print_inst, pyout);
}
free(env);
}
int main(int argc, char *argv[])
{
int k,found;
Digraph g; // graph declaration
string digraph_kpaths_filename, source_node_name, target_node_name;
DiNodeName vname(g); // name of graph nodes
Digraph::NodeMap<double> px(g),py(g); // xy-coodinates for each node
Digraph::NodeMap<int> vcolor(g);// color of nodes
Digraph::ArcMap<int> ecolor(g); // color of edges
ArcWeight lpvar(g); // used to obtain the contents of the LP variables
ArcWeight weight(g); // edge weights
Digraph::ArcMap<GRBVar> x(g); // binary variables for each arc
vector <DiNode> V;
DiNode source,target;
int seed=0;
srand48(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
//set_pdfreader("open -a Skim.app");
// double cutoff; // used to prune non promissing branches (of the B&B tree)
if (argc!=5) {cout<<endl<<"Usage: "<< argv[0]<<" <digraph_kpaths_filename> <source_node_name> <target_node_name> <k>"<< endl << endl;
cout << "Example: " << argv[0] << " digr_triang_sparse_100 12 50 5" << endl << endl;
exit(0);}
digraph_kpaths_filename = argv[1];
source_node_name = argv[2];
target_node_name = argv[3];
k = atoi(argv[4]);
GRBEnv env = GRBEnv();
GRBModel model = GRBModel(env);
model.getEnv().set(GRB_IntParam_Seed, seed);
model.set(GRB_StringAttr_ModelName, "Oriented k-Paths with GUROBI"); // prob. name
model.set(GRB_IntAttr_ModelSense, GRB_MINIMIZE); // is a minimization problem
ReadListDigraph(digraph_kpaths_filename,g,vname,weight,px,py,0);
found=0;
for (DiNodeIt v(g);v!=INVALID;++v)
if(vname[v]==source_node_name){source=v;found=1;break;}
if (!found) {cout<<"Could not find source node "<<source_node_name<<endl;exit(0);}
found=0;
for (DiNodeIt v(g);v!=INVALID;++v)
if(vname[v]==target_node_name){target=v;found=1;break;}
if (!found) {cout<<"Could not find target node "<<target_node_name<<endl;exit(0);}
kPaths_Instance T(g,vname,px,py,weight,source,target,k);
//for (DiNodeIt v(g);v!=INVALID;++v){ if(v==T.V[0])vcolor[v]=RED; else vcolor[v]=BLUE;}
//for (int i=1;i<T.nt;i++) vcolor[T.V[i]] = MAGENTA;
//for (ArcIt e(g); e != INVALID; ++e) ecolor[e] = BLUE;
//ViewListDigraph(g,vname,px,py,vcolor,ecolor,"Triangulated graph");
// Generate the binary variables and the objective function
// Add one binary variable for each edge and set its cost in the objective function
for (Digraph::ArcIt e(g); e != INVALID; ++e) {
char name[100];
sprintf(name,"X_%s_%s",vname[g.source(e)].c_str(),vname[g.target(e)].c_str());
x[e] = model.addVar(0.0, 1.0, weight[e],GRB_CONTINUOUS,name); }
model.update(); // run update to use model inserted variables
try {
//if (time_limit >= 0) model.getEnv().set(GRB_DoubleParam_TimeLimit,time_limit);
//model.getEnv().set(GRB_DoubleParam_ImproveStartTime,10); //try better sol. aft. 10s
// if (cutoff > 0) model.getEnv().set(GRB_DoubleParam_Cutoff, cutoff );
//model.write("model.lp"); system("cat model.lp");
// Add degree constraint for each node (sum of solution edges incident to a node is 2)
for (Digraph::NodeIt v(g); v!=INVALID; ++v) {
GRBLinExpr exprin, exprout;
for (Digraph::InArcIt e(g,v); e != INVALID; ++e) exprin += x[e];
for (Digraph::OutArcIt e(g,v); e != INVALID; ++e) exprout += x[e];
if (v==source) model.addConstr(exprout - exprin == k );
else if (v==target) model.addConstr(exprin - exprout == k );
else model.addConstr(exprin - exprout == 0 );
}
model.optimize();
double soma=0.0;
for (DiNodeIt v(g);v!=INVALID;++v) vcolor[v]=BLUE; // all nodes BLUE
vcolor[source]=RED; // change the colors of the source node
vcolor[target]=RED; // and the target node to RED
for (Digraph::ArcIt e(g); e!=INVALID; ++e) {
lpvar[e] = x[e].get(GRB_DoubleAttr_X);
if (lpvar[e] > 1.0 - EPS) soma += weight[e];
}
cout << "kPaths Tree Value = " << soma << endl;
//-----------------------------------------------------------------
// By Lucas Prado Melo: coloring paths by bfs
bool ok = true;
for(int i=0; i < k && ok; i++) {
queue<DiNode> q;
Digraph::NodeMap<Arc> pre(g, INVALID);
q.push(source);
while (!q.empty()) {
DiNode cur = q.front();
q.pop();
for(Digraph::OutArcIt e(g, cur); e!=INVALID; ++e) {
DiNode nxt = g.runningNode(e);
if (pre[nxt] == INVALID && ecolor[e] == NOCOLOR && lpvar[e] > 1.0 - EPS) {
pre[nxt] = e;
q.push(nxt);
}
}
}
if (pre[target] == INVALID) ok = false;
else {
DiNode x = target;
while (x != source) {
ecolor[pre[x]] = 3+i%6; // use colors 3(RED) to 8(CYAN), see myutils.h
x = g.oppositeNode(x, pre[x]);
}
}
}
if (!ok) {cout << "Nao eh possivel encontrar os " << k << " caminhos!" << endl;}
for(Digraph::ArcIt e(g); e!=INVALID; ++e) {
if (lpvar[e] > 1.0 - EPS && ecolor[e] == NOCOLOR ) {
cout << "Alguma(s) aresta(s) nao pertencem a qualquer caminho!" << endl;
break;
}
}
for(Digraph::ArcIt e(g); e!=INVALID; ++e) {
if (lpvar[e] < 1.0-EPS && lpvar[e] > EPS) {
ecolor[e] = GRAY;
}
}
for(Digraph::ArcIt e(g); e!=INVALID; ++e) {
if (lpvar[e] < 1.0-EPS && lpvar[e] > EPS) {
cout << "Alguma(s) aresta(s) possuem valor fracionario! (marcadas com cor cinza claro)" << endl;
break;
}
}
//-----------------------------------------------------------------
cout << "kPaths Tree Value = " << soma << endl;
ViewListDigraph(g,vname,px,py,vcolor,ecolor,
"minimum kPaths cost in graph with "+IntToString(T.nnodes)+
" nodes and "+IntToString(k)+" paths: "+DoubleToString(soma));
} catch (...) {cout << "Error during callback..." << endl; }
return 0;
}
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
GRBVar min;
GRBVar x[m][n];
min = model.addVar(0, 30000, 1 , GRB_CONTINUOUS, "min");
int i,j;
for(i=0;i<m;i++){
for(j=0;j<n;j++) {
char name[100];
sprintf(name,"I%dM%d",i,j);
x[i][j] = model.addVar(0, 1, 0 , GRB_BINARY, name);
}
}
model.update();
//cada tarefa em 1 máquina
for (int i=0; i<n; i++){
GRBLinExpr sum2;
for (int j=0; j<m; j++){
sum2= sum2 + x[j][i];
}
model.addConstr(sum2 == 1);
}
//min eh pior caso
for(int j=0; j<m; j++){
GRBLinExpr sum3;
for (i=0; i<n ; i++) {
sum3 = sum3 + jobsize[i]*x[j][i]/ machinespeed[j];
}
model.addConstr(min >= sum3);
}
model.update();
model.write("model.lp");
system("cat model.lp");
model.optimize();
// Verifica se obteve solucao otima
if (model.get(GRB_IntAttr_Status) != GRB_OPTIMAL) {
cout << "Erro, sistema impossivel" << endl;
exit(1);
}
for (int i=0; i<n; i++){
for (int j=0; j<m; j++){
if(x[j][i].get(GRB_DoubleAttr_X)>0.999)
tarefa_maquina[i][j] = 1;
else
tarefa_maquina[i][j] = 0;
}
}
/*--------------------------------------------------------------------------------- */
/*--------------------------- 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;
}
// 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[])
{
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;
}
}
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];
GRBVar max = model.addVar(0,10000,1,GRB_CONTINUOUS,"max");
// 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);
// }
//
// 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;
}
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;
}
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[])
{
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;
}
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[]) {
// 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[])
{
Digraph g; // graph declaration
string digraph_matching_filename;
DiNodeName vname(g); // name of graph nodes
Digraph::NodeMap<double> px(g),py(g); // xy-coodinates for each node
Digraph::NodeMap<int> vcolor(g);// color of nodes
Digraph::ArcMap<int> ecolor(g); // color of edges
ArcWeight lpvar(g); // used to obtain the contents of the LP variables
ArcWeight weight(g); // edge weights
srand48(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
// double cutoff; // used to prune non promissing branches (of the B&B tree)
if (argc!=2) {
cout<<endl<<"Usage: "<< argv[0]<<" <digraph_matching_filename>"<<endl<<endl;
cout << "Example: " << argv[0] << " digr_bipartite_100_10" << endl << endl;
exit(0);}
digraph_matching_filename = argv[1];
ReadListDigraph(digraph_matching_filename,g,vname,weight,px,py,0);
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
GRBEnv env = GRBEnv();
GRBModel model = GRBModel(env);
model.set(GRB_IntAttr_ModelSense, GRB_MAXIMIZE); // is a maximization problem
/* LPI variables */
Digraph::ArcMap<GRBVar> x(g); // variable for connections, 1=connected, 0=not connected
GRBLinExpr expressao;
for (Digraph::ArcIt e(g); e != INVALID; ++e) {
x[e] = model.addVar(0.0, 1.0, weight[e], GRB_CONTINUOUS);
// Exercise: Using bipartite graphs, explain why we can use continuous
// variables and still obtain integer solutions
}
model.update();
for (Digraph::NodeIt v(g); v!=INVALID; ++v) {
GRBLinExpr exprin, exprout;
int n_arcs_in=0,n_arcs_out=0;
// for each node, the number of arcs leaving is at most 1
// remember: the graph is bipartite, with arcs going from one part to the other
for (Digraph::InArcIt e(g,v); e != INVALID; ++e) {exprin += x[e]; n_arcs_in++;}
if (n_arcs_in > 0) {model.addConstr(exprin <= 1 ); vcolor[v] = BLUE;}
// for each node, the number of arcs entering is at most 1
for (Digraph::OutArcIt e(g,v); e != INVALID; ++e) {exprout += x[e]; n_arcs_out++;}
if (n_arcs_out > 0) {model.addConstr(exprout <= 1 ); vcolor[v] = RED;}
}
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
try {
model.optimize();
double soma=0.0;
int cor=0;
for (Digraph::ArcIt e(g); e!=INVALID; ++e) {
lpvar[e] = x[e].get(GRB_DoubleAttr_X);
if (lpvar[e] > 1.0 - EPS) { soma += weight[e]; ecolor[e] = (cor % 8) + 2; cor++; }
else ecolor[e] = NOCOLOR; }
cout << "Maximum Bipartite Matching = " << soma << endl;
// Esta rotina precisa do programa neato/dot do Graphviz
ViewListDigraph(g,vname,px,py,vcolor,ecolor,
"maximum weighted matching in graph with "+IntToString(countNodes(g))+
" nodes:"+DoubleToString(soma));
} catch(GRBException e) {
cerr << "Nao foi possivel resolver o PLI." << endl;
cerr << "Codigo de erro = " << e.getErrorCode() << endl;
cerr << e.getMessage();
}
return 0;
}
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(int argc, char *argv[])
{
int nt;
Digraph g; // graph declaration
string digraph_steiner_filename;
DiNodeName vname(g); // name of graph nodes
Digraph::NodeMap<double> px(g),py(g); // xy-coodinates for each node
Digraph::NodeMap<int> vcolor(g);// color of nodes
Digraph::ArcMap<int> ecolor(g); // color of edges
ArcWeight lpvar(g); // used to obtain the contents of the LP variables
ArcWeight weight(g); // edge weights
Digraph::ArcMap<GRBVar> x(g); // binary variables for each arc
vector <DiNode> V;
int seed=0;
srand48(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
// double cutoff; // used to prune non promissing branches (of the B&B tree)
if (argc!=2) {cout<< endl << "Usage: "<< argv[0]<<" <digraph_steiner_filename>"<< endl << endl;
cout << "Examples: " << argv[0] << " gr_berlin52.steiner" << endl;
cout << " " << argv[0] << " gr_usa48.steiner" << endl << endl;
exit(0);}
digraph_steiner_filename = argv[1];
//int time_limit = 3600; // solution must be obtained within time_limit seconds
GRBEnv env = GRBEnv();
GRBModel model = GRBModel(env);
model.getEnv().set(GRB_IntParam_LazyConstraints, 1);
model.getEnv().set(GRB_IntParam_Seed, seed);
model.set(GRB_StringAttr_ModelName, "Oriented Steiner Tree with GUROBI"); // prob. name
model.set(GRB_IntAttr_ModelSense, GRB_MINIMIZE); // is a minimization problem
ReadListDigraphSteiner(digraph_steiner_filename,g,vname,weight,px,py,1,nt,V);
Steiner_Instance T(g,vname,px,py,weight,nt,V);
//for (DiNodeIt v(g);v!=INVALID;++v){ if(v==T.V[0])vcolor[v]=RED; else vcolor[v]=BLUE;}
//for (int i=1;i<T.nt;i++) vcolor[T.V[i]] = MAGENTA;
//for (ArcIt e(g); e != INVALID; ++e) ecolor[e] = BLUE;
//ViewListDigraph(g,vname,px,py,vcolor,ecolor,"Triangulated graph");
// Generate the binary variables and the objective function
// Add one binary variable for each edge and set its cost in the objective function
for (Digraph::ArcIt e(g); e != INVALID; ++e) {
char name[100];
sprintf(name,"X_%s_%s",vname[g.source(e)].c_str(),vname[g.target(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
try {
//if (time_limit >= 0) model.getEnv().set(GRB_DoubleParam_TimeLimit,time_limit);
//model.getEnv().set(GRB_DoubleParam_ImproveStartTime,10); //try better sol. aft. 10s
// if (cutoff > 0) model.getEnv().set(GRB_DoubleParam_Cutoff, cutoff );
ConnectivityCuts cb = ConnectivityCuts(T , x);
model.setCallback(&cb);
model.update();
//model.write("model.lp"); system("cat model.lp");
model.optimize();
double soma=0.0;
for (DiNodeIt v(g);v!=INVALID;++v) vcolor[v]=BLUE; // all nodes BLUE
for (int i=0;i<T.nt;i++) vcolor[T.V[i]]=MAGENTA; // change terminals to MAGENTA
vcolor[T.V[0]]=RED; // change root to RED
for (Digraph::ArcIt e(g); e!=INVALID; ++e) {
lpvar[e] = x[e].get(GRB_DoubleAttr_X);
if (lpvar[e] > 1.0 - EPS) { soma += weight[e]; ecolor[e] = RED; }
else ecolor[e] = NOCOLOR; }
cout << "Steiner Tree Value = " << soma << endl;
ViewListDigraph(g,vname,px,py,vcolor,ecolor,
"Steiner Tree cost in graph with "+IntToString(T.nnodes)+
" nodes and "+IntToString(T.nt)+" terminals: "+DoubleToString(soma));
} catch (...) {cout << "Error during callback..." << endl; }
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;
}
}
}
