91 lines
2.4 KiB
Python
91 lines
2.4 KiB
Python
# Copyright 2011 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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Volsay problem in Google or-tools.
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From the OPL model volsay.mod
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Using arrays.
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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.linear_solver import pywraplp
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def main(unused_argv):
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# Create the solver.
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# using GLPK
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solver = pywraplp.Solver('CoinsGridGLPK',
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pywraplp.Solver.GLPK_LINEAR_PROGRAMMING)
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# Using CLP
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# solver = pywraplp.Solver('CoinsGridCLP',
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# pywraplp.Solver.CLP_LINEAR_PROGRAMMING)
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# data
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num_products = 2
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products = ['Gas', 'Chloride']
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components = ['nitrogen', 'hydrogen', 'chlorine']
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demand = [ [1,3,0], [1,4,1]]
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profit = [30,40]
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stock = [50,180,40]
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# declare variables
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production = [solver.NumVar(0, 100000, 'production[%i]' % i )
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for i in range(num_products)]
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#
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# constraints
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#
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for c in range(len(components)):
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solver.Add(solver.Sum([demand[p][c]*production[p]
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for p in range(len(products)) ]) <= stock[c])
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# objective
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# Note: there is no support for solver.ScalProd in the LP/IP interface
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objective = solver.Maximize(solver.Sum([production[p]*profit[p]
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for p in range(num_products)]))
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print 'NumConstraints:', solver.NumConstraints()
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print 'NumVariables:', solver.NumVariables()
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print
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#
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# solution and search
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#
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solver.Solve()
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print
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print 'objective = ', solver.ObjectiveValue()
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for i in range(num_products):
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print products[i], '=', production[i].SolutionValue(),
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print 'ReducedCost = ', production[i].ReducedCost()
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print
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print 'walltime :', solver.WallTime(), 'ms'
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print 'iterations:', solver.Iterations()
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if __name__ == '__main__':
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main('Volsay')
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