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# Copyright 2011 Hakan Kjellerstrand hakank@gmail.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@gmail.com)
  Also see my other Google CP Solver models:
  http://www.hakank.org/google_or_tools/
"""
from __future__ import print_function
from ortools.linear_solver import pywraplp



# 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(), end=' ')
  print('ReducedCost = ', production[i].ReducedCost())