# 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 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

  products = ['Gas', 'Chloride']
  components = ['nitrogen', 'hydrogen', 'chlorine']

  demand = [ [1,3,0], [1,4,1]]
  profit = [30,40]
  stock = [50,180,40]

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

  #
  # constraints
  #
  for c in range(len(components)):
    solver.Add(solver.Sum([demand[p][c]*production[p]
                           for p in range(len(products)) ]) <= stock[c])


  # objective
  # Note: there is no support for solver.ScalProd in the LP/IP interface
  objective = solver.Maximize(solver.Sum([production[p]*profit[p]
                                          for p in range(num_products)]))


  print 'NumConstraints:', solver.NumConstraints()
  print 'NumVariables:', solver.NumVariables()
  print

  #
  # solution and search
  #
  solver.Solve()

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

  print
  print 'walltime  :', solver.WallTime(), 'ms'
  print 'iterations:', solver.Iterations()


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