116 lines
3.5 KiB
Python
116 lines
3.5 KiB
Python
# Copyright 2010 Hakan Kjellerstrand hakank@bonetmail.com
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Simple diet problem in Google CP Solver.
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Standard Operations Research example in Minizinc
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Minimize the cost for the products:
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Type of Calories Chocolate Sugar Fat
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Food (ounces) (ounces) (ounces)
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Chocolate Cake (1 slice) 400 3 2 2
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Chocolate ice cream (1 scoop) 200 2 2 4
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Cola (1 bottle) 150 0 4 1
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Pineapple cheesecake (1 piece) 500 0 4 5
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Compare with the following models:
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* Tailor/Essence': http://hakank.org/tailor/diet1.eprime
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* MiniZinc: http://hakank.org/minizinc/diet1.mzn
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* SICStus: http://hakank.org/sicstus/diet1.pl
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* Zinc: http://hakank.org/minizinc/diet1.zinc
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* Choco: http://hakank.org/choco/Diet.java
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* Comet: http://hakank.org/comet/diet.co
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* ECLiPSe: http://hakank.org/eclipse/diet.ecl
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* Gecode: http://hakank.org/gecode/diet.cpp
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* Gecode/R: http://hakank.org/gecode_r/diet.rb
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* JaCoP: http://hakank.org/JaCoP/Diet.java
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This model was created by Hakan Kjellerstrand (hakank@bonetmail.com)
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Also see my other Google CP Solver models: http://www.hakank.org/google_or_tools/
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"""
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from ortools.constraint_solver import pywrapcp
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def main(unused_argv):
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# Create the solver.
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solver = pywrapcp.Solver('Diet')
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#
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# data
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#
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n = 4
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price = [ 50, 20, 30, 80] # in cents
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limits = [500, 6, 10, 8] # requirements for each nutrition type
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# nutritions for each product
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calories = [400, 200, 150, 500]
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chocolate = [3,2,0,0]
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sugar = [2,2,4,4]
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fat = [2,4,1,5]
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#
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# declare variables
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#
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x = [solver.IntVar(0, 100, 'x%d' % i) for i in range(n)]
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cost = solver.IntVar(0,10000, 'cost')
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#
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# constraints
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#
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solver.Add(solver.Sum([x[i]*calories[i] for i in range(n)]) >= limits[0])
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solver.Add(solver.Sum([x[i]*chocolate[i] for i in range(n)]) >= limits[1])
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solver.Add(solver.Sum([x[i]*sugar[i] for i in range(n)]) >= limits[2])
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solver.Add(solver.Sum([x[i]*fat[i] for i in range(n)]) >= limits[3])
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# objective
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objective = solver.Minimize(cost, 1)
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#
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# solution
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#
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solution = solver.Assignment()
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solution.AddObjective(cost)
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solution.Add(x)
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# last solution since it's a minimization problem
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collector = solver.LastSolutionCollector(solution)
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search_log = solver.SearchLog(100, cost)
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solver.Solve(solver.Phase(x + [cost],
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solver.INT_VAR_SIMPLE,
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solver.ASSIGN_MIN_VALUE),
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[objective, search_log, collector])
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# get the first (and only) solution
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print "cost:", collector.ObjectiveValue(0)
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print [("abcdefghij"[i],collector.Value(0, x[i])) for i in range(n)]
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print
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print "failures:", solver.Failures()
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print "branches:", solver.Branches()
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print "WallTime:", solver.WallTime()
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print
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if __name__ == '__main__':
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main("cp sample")
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