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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.
"""

  Production planning problem in Google or-tools.

  From the OPL model production.mod.

  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
import sys
from ortools.linear_solver import pywraplp



# Create the solver.

# using GLPK
if sol == 'GLPK':
  solver = pywraplp.Solver('CoinsGridGLPK',
                           pywraplp.Solver.GLPK_LINEAR_PROGRAMMING)
else:
  # Using CLP
  solver = pywraplp.Solver('CoinsGridCLP',
                           pywraplp.Solver.CLP_LINEAR_PROGRAMMING)

#
# data
#
kluski = 0
capellini = 1
fettucine = 2
products = ['kluski', 'capellini', 'fettucine']
num_products = len(products)

flour = 0
eggs = 1
resources = ['flour', 'eggs']
num_resources = len(resources)

consumption = [[0.5, 0.2], [0.4, 0.4], [0.3, 0.6]]
capacity = [20, 40]
demand = [100, 200, 300]
inside_cost = [0.6, 0.8, 0.3]
outside_cost = [0.8, 0.9, 0.4]

#
# declare variables
#
inside = [
    solver.NumVar(0, 10000, 'inside[%i]' % p) for p in range(num_products)
]
outside = [
    solver.NumVar(0, 10000, 'outside[%i]' % p) for p in range(num_products)
]

# to minimize
z = solver.Sum([
    inside_cost[p] * inside[p] + outside_cost[p] * outside[p]
    for p in range(num_products)
])

#
# constraints
#
for r in range(num_resources):
  solver.Add(
      solver.Sum([consumption[p][r] * inside[p]
                  for p in range(num_products)]) <= capacity[r])

for p in range(num_products):
  solver.Add(inside[p] + outside[p] >= demand[p])

objective = solver.Minimize(z)

solver.Solve()

print()
print('z = ', solver.Objective().Value())

for p in range(num_products):
  print(
      products[p],
      ': inside:',
      inside[p].SolutionValue(),
      '(ReducedCost:',
      inside[p].ReducedCost(),
      ')',
      end=' ')
  print('outside:', outside[p].SolutionValue(), ' (ReducedCost:',
        outside[p].ReducedCost(), ')')
print()