Я использую гибкую модель CPSAT от Google or-tools для своего производственного графика. Я добавил нужное ограничение (машина уже работает, пытаясь заставить работника работать), но я все еще не уверен, как изменить переменную основного интервала :( Пожалуйста, помогите. Major n00b, и я собираюсь закончить это Это последнее препятствие для графика производства!
Ниже мой код.
def flexible_jobshop():
# Data part (duration of task, machine) with corresponding alternative machines per task.
jobs = [[[(11, 1), (11, 5)], [(8, 3)]], [[(11, 1), (11, 5)], [(8, 3)]], [[(11, 1), (11, 5)], [(8, 3)]], [[(13, 1), (13, 5)], [(10, 3)]], [[(17, 1), (17, 5)], [(12, 3)]], [[(22, 1), (22, 5)], [(17, 3)]], [[(26, 1), (26, 5)], [(20, 3)], [(33, 2)], [(20, 4)]], [[(12, 1), (12, 5)], [(9, 3)]], [[(28, 1), (28, 5)], [(21, 3)], [(35, 2)]], [[(28, 1), (28, 5)], [(21, 3)]], [[(11, 1), (11, 5)], [(8, 3)]], [[(28, 1), (28, 5)], [(21, 3)], [(35, 2)]], [[(26, 1), (26, 5)], [(19, 3)], [(33, 2)]]]
num_jobs = len(jobs)
all_jobs = range(num_jobs)
num_machines = 5
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[0])
horizon += max_task_duration
print('Horizon = %i' % horizon)
# Global storage of variables.
intervals_per_resources = 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][0]
max_duration = task[0][0]
num_alternatives = len(task)
all_alternatives = range(num_alternatives)
for alt_id in range(1, num_alternatives):
alt_duration = task[alt_id][0]
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(0, horizon, 'start' + suffix_name)
duration = model.NewIntVar(min_duration, max_duration,
'duration' + suffix_name)
end = model.NewIntVar(0, 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:
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.Add(sum(l_presences) == 1)
else:
intervals_per_resources[task[0][1]].append(interval)
presences[(job_id, task_id, 0)] = model.NewIntVar(1, 1, '')
job_ends.append(previous_end)
# Create machines constraints.
for machine_id in intervals in intervals_per_resources.items():
intervals = intervals_per_resources[machine_id]
if len(intervals) > 1:
model.AddNoOverlap(intervals)
# Create workers constraints.
for worker_id, intervals in intervals_per_resources.items():
intervals = intervals_per_resources[worker_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()
status = solver.Solve(model)
# 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, worker_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))