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set_covering

examples/notebook/contrib/set_covering.ipynb

2016-063.1 KB
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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.

set_covering

<table align="left"> <td> <a href="https://colab.research.google.com/github/google/or-tools/blob/main/examples/notebook/contrib/set_covering.ipynb">Run in Google Colab</a> </td> <td> <a href="https://github.com/google/or-tools/blob/main/examples/contrib/set_covering.py">View source on GitHub</a> </td> </table>

First, you must install ortools package in this colab.

python
%pip install ortools

Set covering in Google CP Solver.

Placing of firestations, from Winston 'Operations Research', page 486.

Compare with the following models:

This model was created by Hakan Kjellerstrand ([email protected]) Also see my other Google CP Solver models: http://www.hakank.org/google_or_tools/

python
from ortools.constraint_solver import pywrapcp


def main(unused_argv):

  # Create the solver.
  solver = pywrapcp.Solver("Set covering")

  #
  # data
  #
  min_distance = 15
  num_cities = 6

  distance = [[0, 10, 20, 30, 30, 20], [10, 0, 25, 35, 20, 10],
              [20, 25, 0, 15, 30, 20], [30, 35, 15, 0, 15, 25],
              [30, 20, 30, 15, 0, 14], [20, 10, 20, 25, 14, 0]]

  #
  # declare variables
  #
  x = [solver.IntVar(0, 1, "x[%i]" % i) for i in range(num_cities)]

  #
  # constraints
  #

  # objective to minimize
  z = solver.Sum(x)

  # ensure that all cities are covered
  for i in range(num_cities):
    b = [x[j] for j in range(num_cities) if distance[i][j] <= min_distance]
    solver.Add(solver.SumGreaterOrEqual(b, 1))

  objective = solver.Minimize(z, 1)

  #
  # solution and search
  #
  solution = solver.Assignment()
  solution.Add(x)
  solution.AddObjective(z)

  collector = solver.LastSolutionCollector(solution)
  solver.Solve(
      solver.Phase(x + [z], solver.INT_VAR_DEFAULT, solver.INT_VALUE_DEFAULT),
      [collector, objective])

  print("z:", collector.ObjectiveValue(0))
  print("x:", [collector.Value(0, x[i]) for i in range(num_cities)])

  print("failures:", solver.Failures())
  print("branches:", solver.Branches())
  print("WallTime:", solver.WallTime())


main("cp sample")