e179c8b847
* remove python script * remove RTE actions * fix test_xpress_interface.cc * remove callback_xpress.py * revert writing colnames and rownames * accept suggestion from Mizux * clean * change cmake/README.md * try fix build bazel * try fix build bazel add MPSWriteError.h * xpress tests gracefully exit if Xpress not found * add integer and linear programming test for dotnet python and java * remove MPSWriteError * try fix Window build * remove useless line from CMakeLists.txt * try fix test under windows * reformat * use XPRESS_LP instead of XPRESS for linear programming examples * tools: add --platform arg when possible make script more resilient/cross-platform * [CP-SAT] convert to PEP8 convention * use XPRSmipoptimize and XPRSlpoptimize instead of XPRSminim and XPRSmaxim (#114) * use XPRSmipoptimize and XPRSlpoptimize instead of XPRSminim and XPRSmaxim * clean xpress/environment files * accept changes: empty char* parameter for XPRS*optimize * Add test on number iterations with LP basis * fix gtests flags * refactor * suggestions by @flomnes * remove unwanted files --------- Co-authored-by: Andrea Sgattoni <andrea.sgattoni@rte-france.com> Co-authored-by: Laurent Perron <lperron@google.com> Co-authored-by: Corentin Le Molgat <corentinl@google.com> Co-authored-by: Andrea Sgattoni <andrea.sgattoni@gmail.com>
120 lines
3.7 KiB
Python
Executable File
120 lines
3.7 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2010-2022 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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import collections
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from ortools.sat.colab import visualization
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from ortools.sat.python import cp_model
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def jobshop_ft06() -> None:
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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 = [
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[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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]
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machines = [
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[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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]
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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.new_int_var(0, horizon, "start_%i_%i" % (i, j))
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duration = durations[i][j]
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end_var = model.new_int_var(0, horizon, "end_%i_%i" % (i, j))
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interval_var = model.new_interval_var(
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start_var, duration, end_var, "interval_%i_%i" % (i, j)
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)
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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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)
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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.add_no_overlap(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.new_int_var(0, horizon, "makespan")
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model.add_max_equality(
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obj_var, [all_tasks[(i, machines_count - 1)].end for i in all_jobs]
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)
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model.minimize(obj_var)
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# Solve the 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 the 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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]
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visualization.DisplayJobshop(starts, durations, machines, "FT06")
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else:
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print("Optimal makespan: %i" % solver.objective_value)
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jobshop_ft06()
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