In [ ]:

    

# Copyright 2010 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.
"""

  Simple diet problem using MIP in Google CP Solver.

  Standard Operations Research example.


  Minimize the cost for the products:
  Type of                        Calories   Chocolate    Sugar    Fat
  Food                                      (ounces)     (ounces) (ounces)
  Chocolate Cake (1 slice)       400           3            2      2
  Chocolate ice cream (1 scoop)  200           2            2      4
  Cola (1 bottle)                150           0            4      1
  Pineapple cheesecake (1 piece) 500           0            4      5

  Compare with the CP model:
    http://www.hakank.org/google_or_tools/diet1.py


  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.

print('Solver: ', sol)

if sol == 'GLPK':
  # using GLPK
  solver = pywraplp.Solver('CoinsGridGLPK',
                           pywraplp.Solver.GLPK_MIXED_INTEGER_PROGRAMMING)
else:
  # Using CBC
  solver = pywraplp.Solver('CoinsGridCLP',
                           pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)

#
# data
#
n = 4
price = [50, 20, 30, 80]  # in cents
limits = [500, 6, 10, 8]  # requirements for each nutrition type

# nutritions for each product
calories = [400, 200, 150, 500]
chocolate = [3, 2, 0, 0]
sugar = [2, 2, 4, 4]
fat = [2, 4, 1, 5]

#
# declare variables
#
x = [solver.IntVar(0, 100, 'x%d' % i) for i in range(n)]
cost = solver.Sum([x[i] * price[i] for i in range(n)])

#
# constraints
#
solver.Add(solver.Sum([x[i] * calories[i] for i in range(n)]) >= limits[0])
solver.Add(solver.Sum([x[i] * chocolate[i] for i in range(n)]) >= limits[1])
solver.Add(solver.Sum([x[i] * sugar[i] for i in range(n)]) >= limits[2])
solver.Add(solver.Sum([x[i] * fat[i] for i in range(n)]) >= limits[3])

# objective
objective = solver.Minimize(cost)

#
# solution
#
solver.Solve()

print('Cost:', solver.Objective().Value())
print([int(x[i].SolutionValue()) for i in range(n)])

print()
print('WallTime:', solver.WallTime())
if sol == 'CBC':
  print('iterations:', solver.Iterations())