131 lines
4.7 KiB
Python
131 lines
4.7 KiB
Python
# Copyright 2010-2018 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.
|
|
"""Jobshop with maintenance tasks using the CP-SAT solver."""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import print_function
|
|
|
|
import collections
|
|
|
|
from ortools.sat.python import cp_model
|
|
|
|
|
|
def main():
|
|
"""Solves a jobshop with maintenance on one machine."""
|
|
model = cp_model.CpModel()
|
|
|
|
jobs_data = [ # task = (machine_id, processing_time).
|
|
[(0, 3), (1, 2), (2, 2)], # Job0
|
|
[(0, 2), (2, 1), (1, 4)], # Job1
|
|
[(1, 4), (2, 3)] # Job2
|
|
]
|
|
|
|
machines_count = 1 + max(task[0] for job in jobs_data for task in job)
|
|
all_machines = range(machines_count)
|
|
|
|
# Computes horizon dynamically as the sum of all durations.
|
|
horizon = sum(task[1] for job in jobs_data for task in job)
|
|
|
|
# Named tuple to store information about created variables.
|
|
task_type = collections.namedtuple('Task', 'start end interval')
|
|
# Named tuple to store information about created variables.
|
|
assigned_task_type = collections.namedtuple('assigned_task_type',
|
|
'start job index duration')
|
|
|
|
# Creates job intervals and add to the corresponding machine lists.
|
|
all_tasks = {}
|
|
machine_to_intervals = collections.defaultdict(list)
|
|
|
|
for j, job in enumerate(jobs_data):
|
|
for t, task in enumerate(job):
|
|
suffix = '_%i_%i' % (j, t)
|
|
start_var = model.NewIntVar(0, horizon, 'start' + suffix)
|
|
end_var = model.NewIntVar(0, horizon, 'end' + suffix)
|
|
interval_var = model.NewIntervalVar(start_var, task[1], end_var,
|
|
'interval' + suffix)
|
|
all_tasks[j, t] = task_type(
|
|
start=start_var, end=end_var, interval=interval_var)
|
|
machine_to_intervals[task[0]].append(interval_var)
|
|
|
|
# Add maintenance interval.
|
|
machine_to_intervals[0].append(model.NewIntervalVar(4, 4, 8, 'weekend_0'))
|
|
|
|
# Create disjuctive constraints.
|
|
for m in all_machines:
|
|
model.AddNoOverlap(machine_to_intervals[m])
|
|
|
|
# Precedences inside a job.
|
|
for j, job in enumerate(jobs_data):
|
|
for t in range(len(job) - 1):
|
|
model.Add(all_tasks[j, t + 1].start >= all_tasks[j, t].end)
|
|
|
|
# Makespan objective.
|
|
obj_var = model.NewIntVar(0, horizon, 'makespan')
|
|
model.AddMaxEquality(
|
|
obj_var,
|
|
[all_tasks[j, len(job) - 1].end for j, job in enumerate(jobs_data)])
|
|
model.Minimize(obj_var)
|
|
|
|
# Solve model.
|
|
solver = cp_model.CpSolver()
|
|
status = solver.Solve(model)
|
|
|
|
# Output solution.
|
|
if status == cp_model.OPTIMAL:
|
|
print('Optimal makespan: %i' % solver.ObjectiveValue())
|
|
print()
|
|
|
|
# Create one list of assigned tasks per machine.
|
|
assigned_jobs = collections.defaultdict(list)
|
|
for j, job in enumerate(jobs_data):
|
|
for t, task in enumerate(job):
|
|
machine = task[0]
|
|
assigned_jobs[machine].append(
|
|
assigned_task_type(
|
|
start=solver.Value(all_tasks[j, t].start),
|
|
job=j,
|
|
index=t,
|
|
duration=task[1]))
|
|
|
|
# Create per machine output lines.
|
|
output = ''
|
|
for machine in all_machines:
|
|
# Sort by starting time.
|
|
assigned_jobs[machine].sort()
|
|
sol_line_tasks = ' - machine ' + str(machine) + ': '
|
|
sol_line = ' '
|
|
|
|
for assigned_task in assigned_jobs[machine]:
|
|
name = 'job_%i_%i' % (assigned_task.job, assigned_task.index)
|
|
# Add spaces to output to align columns.
|
|
sol_line_tasks += '%-10s' % name
|
|
start = assigned_task.start
|
|
duration = jobs_data[assigned_task.job][assigned_task.index][1]
|
|
|
|
sol_tmp = '[%i,%i]' % (start, start + duration)
|
|
# Add spaces to output to align columns.
|
|
sol_line += '%-10s' % sol_tmp
|
|
|
|
sol_line += '\n'
|
|
sol_line_tasks += '\n'
|
|
output += sol_line_tasks
|
|
output += sol_line
|
|
|
|
# Finally print the solution found.
|
|
print('Optimal Schedule')
|
|
print(output)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|