Try our new documentation site (beta).


multiobj_c++.cpp


/* Copyright 2020, Gurobi Optimization, LLC */

/* Want to cover three different sets but subject to a common budget of
 * elements allowed to be used. However, the sets have different priorities to
 * be covered; and we tackle this by using multi-objective optimization. */

#include "gurobi_c++.h"
#include <sstream>
#include <iomanip>
using namespace std;

int
main(void)
{
  GRBEnv *env  = 0;
  GRBVar *Elem = 0;
  int e, i, status, nSolutions;

  try{
    // Sample data
    const int groundSetSize = 20;
    const int nSubsets      = 4;
    const int Budget        = 12;
    double Set[][20] =
    { { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 },
      { 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1 },
      { 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0 },
      { 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0 } };
    int    SetObjPriority[] = {3, 2, 2, 1};
    double SetObjWeight[]   = {1.0, 0.25, 1.25, 1.0};

    // Create environment
    env = new GRBEnv("multiobj_c++.log");

    // Create initial model
    GRBModel model = GRBModel(*env);
    model.set(GRB_StringAttr_ModelName, "multiobj_c++");

    // Initialize decision variables for ground set:
    // x[e] == 1 if element e is chosen for the covering.
    Elem = model.addVars(groundSetSize, GRB_BINARY);
    for (e = 0; e < groundSetSize; e++) {
      ostringstream vname;
      vname << "El" << e;
      Elem[e].set(GRB_StringAttr_VarName, vname.str());
    }

    // Constraint: limit total number of elements to be picked to be at most
    // Budget
    GRBLinExpr lhs;
    lhs = 0;
    for (e = 0; e < groundSetSize; e++) {
      lhs += Elem[e];
    }
    model.addConstr(lhs <= Budget, "Budget");

    // Set global sense for ALL objectives
    model.set(GRB_IntAttr_ModelSense, GRB_MAXIMIZE);

    // Limit how many solutions to collect
    model.set(GRB_IntParam_PoolSolutions, 100);

    // Set and configure i-th objective
    for (i = 0; i < nSubsets; i++) {
      GRBLinExpr objn = 0;
      for (e = 0; e < groundSetSize; e++)
        objn += Set[i][e]*Elem[e];
      ostringstream vname;
      vname << "Set" << i;

      model.setObjectiveN(objn, i, SetObjPriority[i], SetObjWeight[i],
                          1.0 + i, 0.01, vname.str());
    }

    // Save problem
    model.write("multiobj_c++.lp");

    // Optimize
    model.optimize();

    // Status checking
    status = model.get(GRB_IntAttr_Status);

    if (status == GRB_INF_OR_UNBD ||
        status == GRB_INFEASIBLE  ||
        status == GRB_UNBOUNDED     ) {
      cout << "The model cannot be solved " <<
             "because it is infeasible or unbounded" << endl;
      return 1;
    }
    if (status != GRB_OPTIMAL) {
      cout << "Optimization was stopped with status " << status << endl;
      return 1;
    }

    // Print best selected set
    cout << "Selected elements in best solution:" << endl << "\t";
    for (e = 0; e < groundSetSize; e++) {
      if (Elem[e].get(GRB_DoubleAttr_X) < .9) continue;
      cout << " El" << e;
    }
    cout << endl;

    // Print number of solutions stored
    nSolutions = model.get(GRB_IntAttr_SolCount);
    cout << "Number of solutions found: " << nSolutions << endl;

    // Print objective values of solutions
    if (nSolutions > 10) nSolutions = 10;
    cout << "Objective values for first " << nSolutions;
    cout << " solutions:" << endl;
    for (i = 0; i < nSubsets; i++) {
      model.set(GRB_IntParam_ObjNumber, i);

      cout << "\tSet" << i;
      for (e = 0; e < nSolutions; e++) {
        cout << " ";
        model.set(GRB_IntParam_SolutionNumber, e);
        double val = model.get(GRB_DoubleAttr_ObjNVal);
        cout << std::setw(6) << val;
      }
      cout << endl;
    }

  }
  catch (GRBException e) {
    cout << "Error code = " << e.getErrorCode() << endl;
    cout << e.getMessage() << endl;
  }
  catch (...) {
    cout << "Exception during optimization" << endl;
  }

  // Free environment/vars
  delete[] Elem;
  delete env;
  return 0;
}

Try Gurobi for Free

Choose the evaluation license that fits you best, and start working with our Expert Team for technical guidance and support.

Evaluation License
Get a free, full-featured license of the Gurobi Optimizer to experience the performance, support, benchmarking and tuning services we provide as part of our product offering.
Academic License
Gurobi supports the teaching and use of optimization within academic institutions. We offer free, full-featured copies of Gurobi for use in class, and for research.
Cloud Trial

Request free trial hours, so you can see how quickly and easily a model can be solved on the cloud.

Search

Gurobi Optimization