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polish output

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Laurent Perron
2018-12-30 12:30:29 +01:00
parent 6c69f549c1
commit 2488775388
2 changed files with 12 additions and 16 deletions
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14
examples/python/jobshop_with_maintenance_sat.py
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@@ -22,6 +22,7 @@ from ortools.sat.python import cp_model
def jobshop_with_maintenance():
"""Solves a jobshop with maintenance on one machine."""
# Create the model.
model = cp_model.CpModel()
jobs_data = [ # task = (machine_id, processing_time).
@@ -38,7 +39,7 @@ def jobshop_with_maintenance():
# Named tuple to store information about created variables.
task_type = collections.namedtuple('Task', 'start end interval')
# Named tuple to store information about created variables.
# Named tuple to manipulate solution information.
assigned_task_type = collections.namedtuple('assigned_task_type',
'start job index duration')
@@ -62,7 +63,7 @@ def jobshop_with_maintenance():
# Add maintenance interval (machine 0 is not available on time {4, 5, 6, 7}).
machine_to_intervals[0].append(model.NewIntervalVar(4, 4, 8, 'weekend_0'))
# Create disjuctive constraints.
# Create and add disjunctive constraints.
for machine in all_machines:
model.AddNoOverlap(machine_to_intervals[machine])
@@ -86,9 +87,6 @@ def jobshop_with_maintenance():
# 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 job_id, job in enumerate(jobs_data):
@@ -106,8 +104,8 @@ def jobshop_with_maintenance():
for machine in all_machines:
# Sort by starting time.
assigned_jobs[machine].sort()
sol_line_tasks = ' - machine ' + str(machine) + ': '
sol_line = ' '
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)
@@ -126,7 +124,7 @@ def jobshop_with_maintenance():
output += sol_line
# Finally print the solution found.
print('Optimal Schedule')
print('Optimal Schedule Length: %i' % solver.ObjectiveValue())
print(output)

14
ortools/sat/samples/minimal_jobshop_sat.py
View File

@@ -40,11 +40,13 @@ def minimal_jobshop_sat():
all_machines = range(machines_count)
# [END data]
# Compute horizon.
# Computes horizon dynamically as the sum of all durations.
horizon = sum(task[1] for job in jobs_data for task in job)
# [START variables]
# Named tuple to store information about created variables.
task_type = collections.namedtuple('task_type', 'start end interval')
# Named tuple to manipulate solution information.
assigned_task_type = collections.namedtuple('assigned_task_type',
'start job index duration')
@@ -96,10 +98,6 @@ def minimal_jobshop_sat():
if status == cp_model.OPTIMAL:
# [START solution_printing]
# Print out makespan.
print('Optimal Schedule Length: %i' % solver.ObjectiveValue())
print()
# Create one list of assigned tasks per machine.
assigned_jobs = collections.defaultdict(list)
for job_id, job in enumerate(jobs_data):
@@ -117,8 +115,8 @@ def minimal_jobshop_sat():
for machine in all_machines:
# Sort by starting time.
assigned_jobs[machine].sort()
sol_line_tasks = ' - machine ' + str(machine) + ': '
sol_line = ' '
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)
@@ -137,7 +135,7 @@ def minimal_jobshop_sat():
output += sol_line
# Finally print the solution found.
print('Optimal Schedule')
print('Optimal Schedule Length: %i' % solver.ObjectiveValue())
print(output)
# [END solution_printing]