assignment_mip.cc

// Copyright 2010-2018 Google LLC
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

// [START program]
// [START import]
#include <vector>

#include "ortools/base/logging.h"
#include "ortools/linear_solver/linear_solver.h"
// [END import]

namespace operations_research {
void AssignmentMip() {
  // Data
  // [START data_model]
  const std::vector<std::vector<double>> costs{
      {90, 80, 75, 70},   {35, 85, 55, 65},   {125, 95, 90, 95},
      {45, 110, 95, 115}, {50, 100, 90, 100},
  };
  const int num_workers = costs.size();
  const int num_tasks = costs[0].size();
  // [END data_model]

  // Solver
  // [START solver]
  // Create the mip solver with the CBC backend.
  MPSolver solver("AssignmentMip",
                  MPSolver::CBC_MIXED_INTEGER_PROGRAMMING);
  // [END solver]

  // Variables
  // [START variables]
  // x[i][j] is an array of 0-1 variables, which will be 1
  // if worker i is assigned to task j.
  std::vector<std::vector<const MPVariable*>> x(
      num_workers, std::vector<const MPVariable*>(num_tasks));
  for (int i = 0; i < num_workers; ++i) {
    for (int j = 0; j < num_tasks; ++j) {
      x[i][j] = solver.MakeIntVar(0, 1, "");
    }
  }
  // [END variables]

  // Constraints
  // [START constraints]
  // Each worker is assigned to at most one task.
  for (int i = 0; i < num_workers; ++i) {
    LinearExpr worker_sum;
    for (int j = 0; j < num_tasks; ++j) {
      worker_sum += x[i][j];
    }
    solver.MakeRowConstraint(worker_sum <= 1.0);
  }
  // Each task is assigned to exactly one worker.
  for (int j = 0; j < num_tasks; ++j) {
    LinearExpr task_sum;
    for (int i = 0; i < num_workers; ++i) {
      task_sum += x[i][j];
    }
    solver.MakeRowConstraint(task_sum == 1.0);
  }
  // [END constraints]

  // Objective.
  // [START objective]
  MPObjective* const objective = solver.MutableObjective();
  for (int i = 0; i < num_workers; ++i) {
    for (int j = 0; j < num_tasks; ++j) {
      objective->SetCoefficient(x[i][j], costs[i][j]);
    }
  }
  objective->SetMinimization();
  // [END objective]

  // Solve
  // [START solve]
  const MPSolver::ResultStatus result_status = solver.Solve();
  // [END solve]

  // Print solution.
  // [START print_solution]
  // Check that the problem has a feasible solution.
  if (result_status != MPSolver::OPTIMAL &
      result_status != MPSolver::FEASIBLE) {
    LOG(FATAL) << "No solution found.";
  }

  LOG(INFO) << "Total cost = " << objective->Value() << "\n\n";

  for (int i = 0; i < num_workers; ++i) {
    for (int j = 0; j < num_tasks; ++j) {
      // Test if x[i][j] is 0 or 1 (with tolerance for floating point
      // arithmetic).
      if (x[i][j]->solution_value() > 0.5) {
        LOG(INFO) << "Worker " << i << " assigned to task " << j
                  << ".  Cost = " << costs[i][j];
      }
    }
  }
  // [END print_solution]
}
}  // namespace operations_research

int main(int argc, char** argv) {
  operations_research::AssignmentMip();
  return EXIT_SUCCESS;
}
// [END program]