# Copyright 2010 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.
"""
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:
* Tailor/Essence': http://hakank.org/tailor/diet1.eprime
* MiniZinc: http://hakank.org/minizinc/diet1.mzn
* SICStus: http://hakank.org/sicstus/diet1.pl
* Zinc: http://hakank.org/minizinc/diet1.zinc
* Choco: http://hakank.org/choco/Diet.java
* Comet: http://hakank.org/comet/diet.co
* ECLiPSe: http://hakank.org/eclipse/diet.ecl
* Gecode: http://hakank.org/gecode/diet.cpp
* Gecode/R: http://hakank.org/gecode_r/diet.rb
* JaCoP: http://hakank.org/JaCoP/Diet.java
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 ortools.constraint_solver import pywrapcp
def main(unused_argv):
# Create the solver.
solver = pywrapcp.Solver("Diet")
#
# 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.IntVar(0, 10000, "cost")
#
# 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, 1)
#
# solution
#
solution = solver.Assignment()
solution.AddObjective(cost)
solution.Add(x)
# last solution since it's a minimization problem
collector = solver.LastSolutionCollector(solution)
search_log = solver.SearchLog(100, cost)
solver.Solve(solver.Phase(x + [cost],
solver.INT_VAR_SIMPLE,
solver.ASSIGN_MIN_VALUE),
[objective, search_log, collector])
# get the first (and only) solution
print "cost:", collector.ObjectiveValue(0)
print [("abcdefghij"[i], collector.Value(0, x[i])) for i in range(n)]
print
print "failures:", solver.Failures()
print "branches:", solver.Branches()
print "WallTime:", solver.WallTime()
print
if __name__ == "__main__":
main("cp sample")