gate scheduling problem

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Laurent Perron
2017-10-17 11:42:24 +02:00
parent a54b49ebb8
commit 2152b57799

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# Copyright 2010-2017 Google
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ortools.sat.python import cp_model
def main():
model = cp_model.CpModel()
jobs = [[3, 3],
[2, 5],
[1, 3],
[3, 7],
[7, 3],
[2, 2],
[2, 2],
[5, 5],
[10, 2],
[4, 3],
[2, 6],
[1, 2],
[6, 8],
[4, 5],
[3, 7]]
max_length = 10
horizon = sum(t[0] for t in jobs)
num_jobs = len(jobs)
all_jobs = range(num_jobs)
intervals = []
intervals0 = []
intervals1 = []
performed = []
starts = []
ends = []
demands = []
for i in all_jobs:
start = model.NewIntVar(0, horizon, 'start_%i' % i)
duration = jobs[i][0]
end = model.NewIntVar(0, horizon, 'end_%i' % i)
interval = model.NewIntervalVar(start, duration, end, 'interval_%i' % i)
starts.append(start)
intervals.append(interval)
ends.append(end)
demands.append(jobs[i][1])
performed_on_m0 = model.NewBoolVar('perform_%i_on_m0' % i)
performed.append(performed_on_m0)
start0 = model.NewIntVar(0, horizon, 'start_%i_on_m0' % i)
end0 = model.NewIntVar(0, horizon, 'end_%i_on_m0' % i)
interval0 = model.NewOptionalIntervalVar(
start0, duration, end0, performed_on_m0, 'interval_%i_on_m0' % i)
intervals0.append(interval0)
start1 = model.NewIntVar(0, horizon, 'start_%i_on_m1' % i)
end1 = model.NewIntVar(0, horizon, 'end_%i_on_m1' % i)
interval1 = model.NewOptionalIntervalVar(
start1, duration, end1, performed_on_m0.Not(), 'interval_%i_on_m1' % i)
intervals1.append(interval1)
# We only propagate the constraint if the tasks is performed on the machine.
model.Add(start0 == start).OnlyEnforceIf(performed_on_m0)
model.Add(start1 == start).OnlyEnforceIf(performed_on_m0.Not())
# Max Length constraint (modeled as a cumulative)
model.AddCumulative(intervals, demands, max_length)
# Choose which machine to perform the jobs on.
model.AddNoOverlap(intervals0)
model.AddNoOverlap(intervals1)
# Objective variable.
makespan = model.NewIntVar(0, horizon, 'makespan')
model.AddMaxEquality(makespan, ends)
model.Minimize(makespan)
# Solve model.
solver = cp_model.CpSolver()
solver.Solve(model)
print('Makespan = %i' % solver.ObjectiveValue())
for i in all_jobs:
performed_machine = 1 - solver.Value(performed[i])
start = solver.Value(starts[i])
print('Job %i starts at %i on machine %i' % (i, start, performed_machine))
if __name__ == '__main__':
main()