107 lines
2.8 KiB
Python
107 lines
2.8 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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Production planning problem in Google or-tools.
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From the OPL model production.mod.
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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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# using GLPK
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if sol == 'GLPK':
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solver = pywraplp.Solver('CoinsGridGLPK',
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pywraplp.Solver.GLPK_LINEAR_PROGRAMMING)
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else:
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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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#
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# data
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#
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kluski = 0
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capellini = 1
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fettucine = 2
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products = ['kluski', 'capellini', 'fettucine']
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num_products = len(products)
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flour = 0
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eggs = 1
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resources = ['flour', 'eggs']
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num_resources = len(resources)
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consumption = [ [0.5, 0.2], [0.4, 0.4], [0.3, 0.6] ]
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capacity = [ 20, 40 ]
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demand = [ 100, 200, 300 ]
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inside_cost = [0.6, 0.8, 0.3 ]
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outside_cost = [0.8, 0.9, 0.4]
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#
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# declare variables
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#
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inside = [solver.NumVar(0, 10000, 'inside[%i]' % p )
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for p in range(num_products)]
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outside = [solver.NumVar(0, 10000, 'outside[%i]' % p )
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for p in range(num_products)]
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# to minimize
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z = solver.Sum([inside_cost[p] * inside[p] + outside_cost[p] * outside[p]
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for p in range(num_products)])
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#
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# constraints
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#
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for r in range(num_resources):
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solver.Add(solver.Sum(
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[consumption[p][r]*inside[p] for p in range(num_products)]) <= capacity[r])
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for p in range(num_products):
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solver.Add(inside[p] + outside[p] >= demand[p])
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objective = solver.Minimize(z)
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solver.Solve()
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print
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print 'z = ', solver.ObjectiveValue()
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for p in range(num_products):
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print products[p], ': inside:', inside[p].SolutionValue(), '(ReducedCost:', inside[p].ReducedCost(), ')',
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print 'outside:', outside[p].SolutionValue(), ' (ReducedCost:', outside[p].ReducedCost(), ')'
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print
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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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