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diet1_mip

<table align="left"> <td> <a href="https://colab.research.google.com/github/google/or-tools/blob/main/examples/notebook/contrib/diet1_mip.ipynb">Run in Google Colab</a> </td> <td> <a href="https://github.com/google/or-tools/blob/main/examples/contrib/diet1_mip.py">View source on GitHub</a> </td> </table>

First, you must install ortools package in this colab.

python

%pip install ortools

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 ([email protected]) Also see my other Google CP Solver models: http://www.hakank.org/google_or_tools/

python

import sys
from ortools.linear_solver import pywraplp


def main(sol='CBC'):

  # Create the solver.

  print('Solver: ', sol)
  solver = pywraplp.Solver.CreateSolver(sol)
  if not solver:
    return

  #
  # 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())



sol = 'CBC'
if len(sys.argv) > 1:
  sol = sys.argv[1]
  if sol != 'GLPK' and sol != 'CBC':
    print('Solver must be either GLPK or CBC')
    sys.exit(1)

main(sol)