8927b03942
Some of these imports are not used. The rest of them only import string to use the string.atoi function. But string.atoi(s) on a string input is identical to just int(s). See the docs: "deprecated since 2.0".
113 lines
3.2 KiB
Python
113 lines
3.2 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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Bus scheduling in Google CP Solver.
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Problem from Taha "Introduction to Operations Research", page 58.
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This is a slightly more general model than Taha's.
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Compare with the following models:
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* MiniZinc: http://www.hakank.org/minizinc/bus_scheduling.mzn
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* Comet : http://www.hakank.org/comet/bus_schedule.co
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* ECLiPSe : http://www.hakank.org/eclipse/bus_schedule.ecl
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* Gecode : http://www.hakank.org/gecode/bus_schedule.cpp
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* Tailor/Essence' : http://www.hakank.org/tailor/bus_schedule.eprime
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* SICStus: http://hakank.org/sicstus/bus_schedule.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:
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http://www.hakank.org/google_or_tools/
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"""
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import sys
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from ortools.constraint_solver import pywrapcp
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def main(num_buses_check=0):
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# Create the solver.
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solver = pywrapcp.Solver("Bus scheduling")
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# data
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time_slots = 6
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demands = [8, 10, 7, 12, 4, 4]
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max_num = sum(demands)
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# declare variables
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x = [solver.IntVar(0, max_num, "x%i" % i) for i in range(time_slots)]
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num_buses = solver.IntVar(0, max_num, "num_buses")
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#
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# constraints
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#
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solver.Add(num_buses == solver.Sum(x))
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# Meet the demands for this and the next time slot
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for i in range(time_slots - 1):
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solver.Add(x[i] + x[i + 1] >= demands[i])
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# The demand "around the clock"
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solver.Add(x[time_slots - 1] + x[0] == demands[time_slots - 1])
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if num_buses_check > 0:
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solver.Add(num_buses == num_buses_check)
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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.Add(num_buses)
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collector = solver.AllSolutionCollector(solution)
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cargs = [collector]
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# objective
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if num_buses_check == 0:
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objective = solver.Minimize(num_buses, 1)
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cargs.extend([objective])
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solver.Solve(solver.Phase(x,
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solver.CHOOSE_FIRST_UNBOUND,
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solver.ASSIGN_MIN_VALUE),
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cargs)
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num_solutions = collector.SolutionCount()
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num_buses_check_value = 0
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for s in range(num_solutions):
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print "x:", [collector.Value(s, x[i]) for i in range(len(x))],
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num_buses_check_value = collector.Value(s, num_buses)
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print " num_buses:", num_buses_check_value
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print
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print "num_solutions:", num_solutions
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print "failures:", solver.Failures()
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print "branches:", solver.Branches()
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print "WallTime:", solver.WallTime()
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print
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if num_buses_check == 0:
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return num_buses_check_value
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if __name__ == "__main__":
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print "Check for minimun number of buses"
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num_buses_check = main()
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print "... got ", num_buses_check, "buses"
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print "All solutions:"
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main(num_buses_check)
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