b027e57e95
- Currently not implemented... Add abseil patch - Add patches/absl-config.cmake Makefile: Add abseil-cpp on unix - Force abseil-cpp SHA1 to 45221cc note: Just before the PR #136 which break all CMake Makefile: Add abseil-cpp on windows - Force abseil-cpp SHA1 to 45221cc note: Just before the PR #136 which break all CMake CMake: Add abseil-cpp - Force abseil-cpp SHA1 to 45221cc note: Just before the PR #136 which break all CMake port to absl: C++ Part - Fix warning with the use of ABSL_MUST_USE_RESULT > The macro must appear as the very first part of a function declaration or definition: ... Note: past advice was to place the macro after the argument list. src: dependencies/sources/abseil-cpp-master/absl/base/attributes.h:418 - Rename enum after windows clash - Remove non compact table constraints - Change index type from int64 to int in routing library - Fix file_nonport compilation on windows - Fix another naming conflict with windows (NO_ERROR is a macro) - Cleanup hash containers; work on sat internals - Add optional_boolean sub-proto Sync cpp examples with internal code - reenable issue173 after reducing number of loops port to absl: Python Part - Add back cp_model.INT32_MIN|MAX for examples Update Python examples - Add random_tsp.py - Run words_square example - Run magic_square in python tests port to absl: Java Part - Fix compilation of the new routing parameters in java - Protect some code from SWIG parsing Update Java Examples port to absl: .Net Part Update .Net examples work on sat internals; Add C++ CP-SAT CpModelBuilder API; update sample code and recipes to use the new API; sync with internal code Remove VS 2015 in Appveyor-CI - abseil-cpp does not support VS 2015... improve tables upgrade C++ sat examples to use the new API; work on sat internals update license dates rewrite jobshop_ft06_distance.py to use the CP-SAT solver rename last example revert last commit more work on SAT internals fix
91 lines
2.8 KiB
Python
91 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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import argparse
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from ortools.constraint_solver import pywrapcp
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--data', default='examples/data/bacp/bacp12.txt', help='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(args):
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#------------------solver and variable declaration-------------
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credits, nb_periods, prereq = ReadData(args.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 = [
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solver.IntVar(0, nb_periods - 1, 'x' + str(i)) for i in range(nb_courses)
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]
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load_vars = [
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solver.IntVar(0, sum(credits), 'load_vars' + str(i))
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for i in range(nb_periods)
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]
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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, 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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main(parser.parse_args())
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