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diet1.py
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diet1.py
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# Copyright 2010 Hakan Kjellerstrand [email protected]
#
# 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 ([email protected])
Also see my other Google CP Solver models:
http://www.hakank.org/google_or_tools/
"""
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()
if __name__ == "__main__":
main("cp sample")