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diet1

examples/notebook/contrib/diet1.ipynb

2016-063.7 KB
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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

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diet1

<table align="left"> <td> <a href="https://colab.research.google.com/github/google/or-tools/blob/main/examples/notebook/contrib/diet1.ipynb">Run in Google Colab</a> </td> <td> <a href="https://github.com/google/or-tools/blob/main/examples/contrib/diet1.py">View source on GitHub</a> </td> </table>

First, you must install ortools package in this colab.

python
%pip install ortools

Simple diet problem in Google CP Solver.

Standard Operations Research example in Minizinc

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 following models:

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
from ortools.sat.python import cp_model


def main(unused_argv):
  # Create the solver.
  model = cp_model.CpModel()

  #
  # 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 = [model.NewIntVar(0, 100, "x%d" % i) for i in range(n)]
  cost = model.NewIntVar(0, 10000, "cost")

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

  # objective
  model.Minimize(cost)

  # Solve model.
  solver = cp_model.CpSolver()
  status = solver.Solve(model)

  # Output solution.
  if status == cp_model.OPTIMAL:
    print("cost:", solver.ObjectiveValue())
    print([("abcdefghij" [i], solver.Value(x[i])) for i in range(n)])
    print()
    print('  - status          : %s' % solver.StatusName(status))
    print('  - conflicts       : %i' % solver.NumConflicts())
    print('  - branches        : %i' % solver.NumBranches())
    print('  - wall time       : %f ms' % solver.WallTime())
    print()


main("cp sample")