For flexible jobshop problem, getting infeasible solution - or-tools

Question

wanted to discuss one issue i am getting while using the google ortools.
Problem: status of the solution is coming INFEASIBLE. but solution is feasible as per my understandings.
below is the sample.py used
import collections
from ortools.sat.python import cp_model
class SolutionPrinter(cp_model.CpSolverSolutionCallback):
"""Print intermediate solutions."""
def __init__(self):
cp_model.CpSolverSolutionCallback.__init__(self)
self.__solution_count = 0
def on_solution_callback(self):
"""Called at each new solution."""
print('Solution %i, time = %f s, objective = %i' %
(self.__solution_count, self.WallTime(), self.ObjectiveValue()))
self.__solution_count += 1
def flexible_jobshop():
"""Solve a small flexible jobshop problem."""
# Data part.
jobs = [
[ # Job 0 (machine_id, processingtime, starttime,endtime)
[(0, 65, 0, 72), (1, 65, 0, 72), (2, 65, 0, 72)], # task 0 with 3 alternatives
[(3, 170, 0, 240), (4, 170, 0, 240), (5, 170, 0, 240)] # task 1 with 3 alternatives
],
[ # Job 1
[(0, 25, 0, 120), (1, 25, 0, 120), (2, 25, 0, 120)],
[(3, 168, 0, 288), (4, 168, 0, 288), (5, 168, 0, 288)]
],
[ # Job 2
[(0, 34, 0, 480), (1, 34, 0, 480), (2, 34, 0, 480)],
[(3, 32, 0, 504), (4, 32, 0, 504), (5, 32, 0, 504)]
],
[ # Job 3
[(0, 39, 0, 600), (1, 39, 0, 600), (2, 39, 0, 600)],
[(3, 93, 0, 696), (4, 93, 0, 696), (5, 93, 0, 696)]
]
]
# from data import Data
# jobs = Data().p3
# print(jobs)
num_jobs = len(jobs)
all_jobs = range(num_jobs)
num_machines = 3
all_machines = range(num_machines)
# Model the flexible jobshop problem.
model = cp_model.CpModel()
horizon = 0
for job in jobs:
for task in job:
max_task_duration = 0
for alternative in task:
max_task_duration = max(max_task_duration, alternative[1])
horizon += max_task_duration
print('Horizon = %i' % horizon)
# Global storage of variables.
intervals_per_resources = collections.defaultdict(list)
starts = {} # indexed by (job_id, task_id).
presences = {} # indexed by (job_id, task_id, alt_id).
job_ends = []
# Scan the jobs and create the relevant variables and intervals.
for job_id in all_jobs:
job = jobs[job_id]
num_tasks = len(job)
previous_end = None
for task_id in range(num_tasks):
task = job[task_id]
min_duration = task[0][1]
max_duration = task[0][1]
num_alternatives = len(task)
all_alternatives = range(num_alternatives)
for alt_id in range(1, num_alternatives):
alt_duration = task[alt_id][1]
min_duration = min(min_duration, alt_duration)
max_duration = max(max_duration, alt_duration)
# Create main interval for the task.
suffix_name = '_j%i_t%i' % (job_id, task_id)
# start = model.NewIntVar(task[0][2], horizon, 'start' + suffix_name)
start = model.NewIntVar(0, horizon, 'start' + suffix_name)
duration = model.NewIntVar(min_duration, max_duration,
'duration' + suffix_name)
end = model.NewIntVar(0, horizon, 'end' + suffix_name)
# end = model.NewIntVar(task[0][3], horizon, 'end' + suffix_name)
interval = model.NewIntervalVar(start, duration, end,
'interval' + suffix_name)
# Store the start for the solution.
starts[(job_id, task_id)] = start
# Add precedence with previous task in the same job.
if previous_end is not None:
model.Add(start >= previous_end)
previous_end = end
# Create alternative intervals.
if num_alternatives > 1:
l_presences = []
for alt_id in all_alternatives:
alt_suffix = '_j%i_t%i_a%i' % (job_id, task_id, alt_id)
l_presence = model.NewBoolVar('presence' + alt_suffix)
l_start = model.NewIntVar(0, horizon, 'start' + alt_suffix)
l_duration = task[alt_id][0]
l_end = model.NewIntVar(0, horizon, 'end' + alt_suffix)
l_interval = model.NewOptionalIntervalVar(
l_start, l_duration, l_end, l_presence,
'interval' + alt_suffix)
l_presences.append(l_presence)
# Link the master variables with the local ones.
model.Add(start == l_start).OnlyEnforceIf(l_presence)
model.Add(duration == l_duration).OnlyEnforceIf(l_presence)
model.Add(end == l_end).OnlyEnforceIf(l_presence)
# Add the local interval to the right machine.
intervals_per_resources[task[alt_id][1]].append(l_interval)
# Store the presences for the solution.
presences[(job_id, task_id, alt_id)] = l_presence
# Select exactly one presence variable.
model.AddExactlyOne(l_presences)
else:
intervals_per_resources[task[0][1]].append(interval)
presences[(job_id, task_id, 0)] = model.NewConstant(1)
job_ends.append(previous_end)
# Create machines constraints.
for machine_id in all_machines:
intervals = intervals_per_resources[machine_id]
if len(intervals) > 1:
model.AddNoOverlap(intervals)
# Makespan objective
makespan = model.NewIntVar(0, horizon, 'makespan')
model.AddMaxEquality(makespan, job_ends)
model.Minimize(makespan)
# Solve model.
solver = cp_model.CpSolver()
solution_printer = SolutionPrinter()
status = solver.Solve(model, solution_printer)
# Print final solution.
for job_id in all_jobs:
print('Job %i:' % job_id)
for task_id in range(len(jobs[job_id])):
start_value = solver.Value(starts[(job_id, task_id)])
machine = -1
duration = -1
selected = -1
for alt_id in range(len(jobs[job_id][task_id])):
if solver.Value(presences[(job_id, task_id, alt_id)]):
duration = jobs[job_id][task_id][alt_id][0]
machine = jobs[job_id][task_id][alt_id][1]
selected = alt_id
print(
' task_%i_%i starts at %i (alt %i, machine %i, duration %i)' %
(job_id, task_id, start_value, selected, machine, duration))
print('Solve status: %s' % solver.StatusName(status))
print('Optimal objective value: %i' % solver.ObjectiveValue())
print('Statistics')
print(' - conflicts : %i' % solver.NumConflicts())
print(' - branches : %i' % solver.NumBranches())
print(' - wall time : %f s' % solver.WallTime())
flexible_jobshop()
for this data set, it is giving infeasible solution.
Not sure what i m doing wrong here.
please guide.