98 lines
2.7 KiB
Python
98 lines
2.7 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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Set covering in Google CP Solver.
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Placing of firestations, from Winston 'Operations Research', page 486.
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Compare with the following models:
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* MiniZinc: http://www.hakank.org/minizinc/set_covering.mzn
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* ECLiPSe : http://www.hakank.org/eclipse/set_covering.ecl
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* Comet : http://www.hakank.org/comet/set_covering.co
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* Gecode : http://www.hakank.org/gecode/set_covering.cpp
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* SICStus : http://www.hakank.org/sicstus/set_covering.pl
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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 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('Set covering')
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#
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# data
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#
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min_distance = 15
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num_cities = 6
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distance = [
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[ 0,10,20,30,30,20],
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[10, 0,25,35,20,10],
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[20,25, 0,15,30,20],
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[30,35,15, 0,15,25],
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[30,20,30,15, 0,14],
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[20,10,20,25,14, 0]
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]
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#
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# declare variables
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#
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x = [solver.IntVar(0,1, 'x[%i]' % i) for i in range(num_cities)]
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#
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# constraints
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#
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# objective to minimize
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z = solver.Sum(x)
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# ensure that all cities are covered
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for i in range(num_cities):
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b = [x[j] for j in range(num_cities) if distance[i][j] <= min_distance]
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solver.Add(solver.SumGreaterOrEqual(b, 1))
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objective = solver.Minimize(z, 1)
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#
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# solution and search
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#
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solution = solver.Assignment()
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solution.Add(x)
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solution.AddObjective(z)
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collector = solver.LastSolutionCollector(solution)
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solver.Solve(solver.Phase(x + [z],
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solver.INT_VAR_DEFAULT,
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solver.INT_VALUE_DEFAULT),
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[collector, objective])
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print "z:", collector.objective_value(0)
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print "x:", [collector.Value(0, x[i]) for i in range(num_cities)]
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print "failures:", solver.failures()
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print "branches:", solver.branches()
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print "wall_time:", solver.wall_time()
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if __name__ == '__main__':
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main("cp sample")
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