88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
# Copyright 2010 Pierre Schaus pschaus@gmail.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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from google.apputils import app
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import gflags
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from constraint_solver import pywrapcp
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FLAGS = gflags.FLAGS
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gflags.DEFINE_string('data', 'python/data/bacp/bacp12.txt',
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'path to data file')
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#----------------helper for binpacking posting----------------
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def BinPacking(solver, binvars, weights, loadvars):
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'''post the load constraint on bins.
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constraints forall j: loadvars[j] == sum_i (binvars[i] == j) * weights[i])
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'''
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pack = solver.Pack(binvars, len(loadvars))
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pack.AddWeightedSumEqualVarDimension(weights, loadvars);
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solver.Add(pack)
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solver.Add(solver.SumEquality(loadvars, sum(weights)))
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#------------------------------data reading-------------------
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def ReadData(filename):
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'''Read data from <filename>.'''
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f = open(filename)
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nb_courses, nb_periods, min_credit, max_credit, nb_prereqs =\
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[int(nb) for nb in f.readline().split()]
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credits = [int(nb) for nb in f.readline().split()]
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prereq = [int(nb) for nb in f.readline().split()]
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prereq = [(prereq[i * 2],prereq[i * 2 + 1]) for i in range(nb_prereqs)]
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return (credits ,nb_periods, prereq)
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def main(unused_argv):
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#------------------solver and variable declaration-------------
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credits ,nb_periods, prereq = ReadData(FLAGS.data)
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nb_courses = len(credits)
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solver = pywrapcp.Solver('Balanced Academic Curriculum Problem')
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x = [solver.IntVar(0, nb_periods - 1, 'x' + str(i))
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for i in range(nb_courses)]
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load_vars = [solver.IntVar(0, sum(credits), 'load_vars' + str(i))
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for i in range(nb_periods)]
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#-------------------post of the constraints--------------
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# Bin Packing.
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BinPacking(solver, x, credits, load_vars)
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# Add dependencies.
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for i,j in prereq:
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solver.Add(x[i] < x[j])
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#----------------Objective-------------------------------
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objective_var = solver.Max(load_vars)
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objective = solver.Minimize(objective_var, 1)
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#------------start the search and optimization-----------
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db = solver.Phase(x,
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solver.CHOOSE_MIN_SIZE_LOWEST_MIN,
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solver.INT_VALUE_DEFAULT)
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search_log = solver.SearchLog(100000, objective_var)
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solver.Solve(db, [objective, search_log])
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if __name__ == '__main__':
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app.run()
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