117 lines
3.1 KiB
Python
117 lines
3.1 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 using MIP in Google CP Solver.
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Standard Operations Research example.
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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 CP model:
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http://www.hakank.org/google_or_tools/diet1.py
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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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import sys
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from ortools.linear_solver import pywraplp
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def main(sol = 'GLPK'):
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# Create the solver.
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print 'Solver: ', sol
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if sol == 'GLPK':
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# using GLPK
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solver = pywraplp.Solver('CoinsGridGLPK',
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pywraplp.Solver.GLPK_MIXED_INTEGER_PROGRAMMING)
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else:
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# Using CBC
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solver = pywraplp.Solver('CoinsGridCLP',
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pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)
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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.Sum([x[i]*price[i] for i in range(n)])
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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]
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for i in range(n)]) >= limits[0])
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solver.Add(solver.Sum([x[i]*chocolate[i]
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for i in range(n)]) >= limits[1])
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solver.Add(solver.Sum([x[i]*sugar[i]
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for i in range(n)]) >= limits[2])
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solver.Add(solver.Sum([x[i]*fat[i]
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for i in range(n)]) >= limits[3])
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# objective
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objective = solver.Minimize(cost)
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#
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# solution
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#
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solver.Solve()
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print "Cost:", solver.Objective().Value()
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print [int(x[i].SolutionValue()) for i in range(n)]
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print
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print "WallTime:", solver.WallTime()
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if sol == 'CBC':
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print 'iterations:', solver.Iterations()
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if __name__ == '__main__':
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sol = 'GLPK'
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if len(sys.argv) > 1:
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sol = sys.argv[1]
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if sol != 'GLPK' and sol != 'CBC':
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print 'Solver must be either GLPK or CBC'
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sys.exit(1)
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main(sol)
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