# Copyright 2011 Hakan Kjellerstrand hakank@bonetmail.com
#
# 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.

"""

  Volsay problem in Google or-tools.

  From the OPL model volsay.mod
  Using arrays.

  This model was created by Hakan Kjellerstrand (hakank@bonetmail.com)
  Also see my other Google CP Solver models:
  http://www.hakank.org/google_or_tools/
"""

from ortools.linear_solver import pywraplp


def main(unused_argv):

  # Create the solver.

  # using GLPK
  solver = pywraplp.Solver('CoinsGridGLPK',
                           pywraplp.Solver.GLPK_LINEAR_PROGRAMMING)

  # Using CLP
  # solver = pywraplp.Solver('CoinsGridCLP',
  #                          pywraplp.Solver.CLP_LINEAR_PROGRAMMING)

  # data
  num_products = 2
  Gas = 0
  Chloride = 1

  products = ['Gas', 'Chloride']

  # declare variables
  production = [solver.NumVar(0, 100000, 'production[%i]' % i)
                for i in range(num_products)]

  #
  # constraints
  #
  solver.Add(production[Gas] + production[Chloride] <= 50)
  solver.Add(3 * production[Gas] + 4 * production[Chloride] <= 180)

  # objective
  objective = solver.Maximize(40 * production[Gas] + 50 * production[Chloride])

  print 'NumConstraints:', solver.NumConstraints()

  #
  # solution and search
  #
  solver.Solve()

  print
  print 'objective = ', solver.Objective().Value()
  for i in range(num_products):
    print products[i], '=', production[i].SolutionValue(),
    print 'ReducedCost = ', production[i].ReducedCost()

if __name__ == '__main__':
  main('Volsay')