71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
from ortools.sat.python import cp_model
|
|
|
|
|
|
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)
|
|
print(solver.ObjectiveValue())
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|