113 lines
3.4 KiB
Python
113 lines
3.4 KiB
Python
# Copyright 2010-2011 Google
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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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"""This model implements a simple jobshop named ft06.
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A jobshop is a standard scheduling problem when you must sequence a
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series of tasks on a set of machines. Each job contains one task per
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machine. The order of execution and the length of each job on each
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machine is task dependent.
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The objective is to minimize the maximum completion time of all
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jobs. This is called the makespan.
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"""
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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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def main(unused_argv):
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# Create the solver.
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solver = pywrapcp.Solver('jobshop ft06')
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machines_count = 6
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jobs_count = 6
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all_machines = range(0, machines_count)
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all_jobs = range(0, jobs_count)
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durations = [[1, 3, 6, 7, 3, 6],
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[8, 5, 10, 10, 10, 4],
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[5, 4, 8, 9, 1, 7],
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[5, 5, 5, 3, 8, 9],
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[9, 3, 5, 4, 3, 1],
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[3, 3, 9, 10, 4, 1]]
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machines = [[2, 0, 1, 3, 5, 4],
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[1, 2, 4, 5, 0, 3],
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[2, 3, 5, 0, 1, 4],
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[1, 0, 2, 3, 4, 5],
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[2, 1, 4, 5, 0, 3],
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[1, 3, 5, 0, 4, 2]]
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# Compute horizon dynamically.
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horizon = sum([sum(durations[i]) for i in all_jobs])
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# Create jobs.
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all_tasks = {}
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for i in all_jobs:
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for j in all_machines:
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all_tasks[(i, j)] = solver.FixedDurationIntervalVar(0,
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horizon,
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durations[i][j],
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False,
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'Job_%i_%i' % (i, j))
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# Create sequence constraints.
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all_sequences = {}
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for i in all_machines:
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machines_jobs = []
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for j in all_jobs:
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for k in all_machines:
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if machines[j][k] == i:
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machines_jobs.append(all_tasks[(j, k)])
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all_sequences[i] = solver.Sequence(machines_jobs, 'machine %i' % i)
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# Makespan objective.
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obj_var = solver.Max([all_tasks[(i, machines_count - 1)].EndExpr()
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for i in all_jobs])
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objective = solver.Minimize(obj_var, 1)
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# Precedences inside a job.
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for i in all_jobs:
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for j in range(0, machines_count - 1):
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solver.Add(all_tasks[(i, j + 1)].StartsAfterEnd(all_tasks[(i, j)]))
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# Add sequence constraints.
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for i in all_machines:
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solver.Add(all_sequences[i])
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# Create search phases.
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vars_phase = solver.Phase([obj_var],
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solver.CHOOSE_FIRST_UNBOUND,
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solver.ASSIGN_MIN_VALUE)
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sequence_phase = solver.Phase([all_sequences[i] for i in all_machines],
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solver.SEQUENCE_DEFAULT)
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main_phase = solver.Compose([sequence_phase, vars_phase])
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# Create the search log.
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search_log = solver.SearchLog(100, obj_var)
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# And solve.
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solver.Solve(main_phase, [search_log, objective])
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if __name__ == '__main__':
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app.run()
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