103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
# Copyright 2010-2018 Google LLC
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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 task_types on a set of machines. Each job contains one task_type 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_type 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 __future__ import print_function
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import collections
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from ortools.sat.python import visualization
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from ortools.sat.python import cp_model
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def jobshop_ft06():
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"""Solves the ft06 jobshop."""
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# Creates the solver.
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model = cp_model.CpModel()
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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], [8, 5, 10, 10, 10, 4], [5, 4, 8, 9, 1, 7],
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[5, 5, 5, 3, 8, 9], [9, 3, 5, 4, 3, 1], [3, 3, 9, 10, 4, 1]]
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machines = [[2, 0, 1, 3, 5, 4], [1, 2, 4, 5, 0, 3], [2, 3, 5, 0, 1, 4],
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[1, 0, 2, 3, 4, 5], [2, 1, 4, 5, 0, 3], [1, 3, 5, 0, 4, 2]]
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# Computes horizon dynamically.
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horizon = sum([sum(durations[i]) for i in all_jobs])
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task_type = collections.namedtuple('task_type', 'start end interval')
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# Creates 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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start_var = model.NewIntVar(0, horizon, 'start_%i_%i' % (i, j))
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duration = durations[i][j]
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end_var = model.NewIntVar(0, horizon, 'end_%i_%i' % (i, j))
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interval_var = model.NewIntervalVar(start_var, duration, end_var,
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'interval_%i_%i' % (i, j))
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all_tasks[(i, j)] = task_type(
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start=start_var, end=end_var, interval=interval_var)
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# Create disjuctive constraints.
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machine_to_jobs = {}
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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)].interval)
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machine_to_jobs[i] = machines_jobs
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model.AddNoOverlap(machines_jobs)
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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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model.Add(all_tasks[(i, j + 1)].start >= all_tasks[(i, j)].end)
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# Makespan objective.
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obj_var = model.NewIntVar(0, horizon, 'makespan')
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model.AddMaxEquality(
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obj_var, [all_tasks[(i, machines_count - 1)].end for i in all_jobs])
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model.Minimize(obj_var)
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# Solve model.
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solver = cp_model.CpSolver()
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solver.parameters.log_search_progress = True
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status = solver.Solve(model)
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# Output solution.
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if status == cp_model.OPTIMAL:
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if visualization.RunFromIPython():
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starts = [[
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solver.Value(all_tasks[(i, j)][0]) for j in all_machines
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] for i in all_jobs]
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visualization.DisplayJobshop(starts, durations, machines, 'FT06')
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else:
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print('Optimal makespan: %i' % solver.ObjectiveValue())
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jobshop_ft06()
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