def main():
# Creates the solver.
model = cp_model.CpModel()
machines_count = 6
jobs_count = 6
all_machines = range(0, machines_count)
all_jobs = range(0, jobs_count)
durations = [[1, 3, 6, 7, 3, 6], [8, 5, 10, 10, 10, 4], [5, 4, 8, 9, 1, 7],
[5, 5, 5, 3, 8, 9], [9, 3, 5, 4, 3, 1], [3, 3, 9, 10, 4, 1]]
machines = [[2, 0, 1, 3, 5, 4], [1, 2, 4, 5, 0, 3], [2, 3, 5, 0, 1, 4],
[1, 0, 2, 3, 4, 5], [2, 1, 4, 5, 0, 3], [1, 3, 5, 0, 4, 2]]
# Computes horizon dynamically.
horizon = sum([sum(durations[i]) for i in all_jobs])
# Creates jobs.
all_tasks = {}
for i in all_jobs:
for j in all_machines:
start = model.NewIntVar(0, horizon, 'start_%i_%i' % (i, j))
duration = durations[i][j]
end = model.NewIntVar(0, horizon, 'end_%i_%i' % (i, j))
interval = model.NewIntervalVar(start, duration, end,
'interval_%i_%i' % (i, j))
all_tasks[(i, j)] = (start, end, interval)
# Create disjuctive constraints.
machine_to_jobs = {}
for i in all_machines:
machines_jobs = []
for j in all_jobs:
for k in all_machines:
if machines[j][k] == i:
machines_jobs.append(all_tasks[(j, k)][2])
machine_to_jobs[i] = machines_jobs
model.AddNoOverlap(machines_jobs)
# Precedences inside a job.
for i in all_jobs:
for j in range(0, machines_count - 1):
model.Add(all_tasks[(i, j + 1)][0] >= all_tasks[(i, j)][1])
# Makespan objective.
obj_var = model.NewIntVar(0, horizon, 'makespan')
model.AddMaxEquality(
obj_var, [all_tasks[(i, machines_count - 1)][1] for i in all_jobs])
model.Minimize(obj_var)
# Solve model.
solver = cp_model.CpSolver()
response = solver.Solve(model)
# Output solution.
if visualization.RunFromIPython():
starts = [[solver.Value(all_tasks[(i, j)][0]) for j in all_machines]
for i in all_jobs]
visualization.DisplayJobshop(starts, durations, machines, 'FT06')
else:
print('Optimal makespan: %i' % solver.ObjectiveValue())
def jobshop_ft06():
"""Solves the ft06 jobshop."""
# Creates the solver.
model = cp_model.CpModel()
machines_count = 6
jobs_count = 6
all_machines = range(0, machines_count)
all_jobs = range(0, jobs_count)
durations = [[1, 3, 6, 7, 3, 6], [8, 5, 10, 10, 10, 4], [5, 4, 8, 9, 1, 7],
[5, 5, 5, 3, 8, 9], [9, 3, 5, 4, 3, 1], [3, 3, 9, 10, 4, 1]]
machines = [[2, 0, 1, 3, 5, 4], [1, 2, 4, 5, 0, 3], [2, 3, 5, 0, 1, 4],
[1, 0, 2, 3, 4, 5], [2, 1, 4, 5, 0, 3], [1, 3, 5, 0, 4, 2]]
# Computes horizon dynamically.
horizon = sum([sum(durations[i]) for i in all_jobs])
task_type = collections.namedtuple('task_type', 'start end interval')
# Creates jobs.
all_tasks = {}
for i in all_jobs:
for j in all_machines:
start_var = model.NewIntVar(0, horizon, 'start_%i_%i' % (i, j))
duration = durations[i][j]
end_var = model.NewIntVar(0, horizon, 'end_%i_%i' % (i, j))
interval_var = model.NewIntervalVar(start_var, duration, end_var,
'interval_%i_%i' % (i, j))
all_tasks[(i, j)] = task_type(start=start_var,
end=end_var,
interval=interval_var)
# Create disjuctive constraints.
machine_to_jobs = {}
for i in all_machines:
machines_jobs = []
for j in all_jobs:
for k in all_machines:
if machines[j][k] == i:
machines_jobs.append(all_tasks[(j, k)].interval)
machine_to_jobs[i] = machines_jobs
model.AddNoOverlap(machines_jobs)
# Precedences inside a job.
for i in all_jobs:
for j in range(0, machines_count - 1):
model.Add(all_tasks[(i, j + 1)].start >= all_tasks[(i, j)].end)
# Makespan objective.
obj_var = model.NewIntVar(0, horizon, 'makespan')
model.AddMaxEquality(
obj_var, [all_tasks[(i, machines_count - 1)].end for i in all_jobs])
model.Minimize(obj_var)
# Solve model.
solver = cp_model.CpSolver()
solver.parameters.log_search_progress = True
status = solver.Solve(model)
# Output solution.
if status == cp_model.OPTIMAL:
if visualization.RunFromIPython():
starts = [[
solver.Value(all_tasks[(i, j)][0]) for j in all_machines
] for i in all_jobs]
visualization.DisplayJobshop(starts, durations, machines, 'FT06')
else:
print('Optimal makespan: %i' % solver.ObjectiveValue())