I want to schedule n jobs on m machines with transition times and alternatives and modified the example in github/or-tools/scheduling_with_transitions_sat.py because I also want some kind of lateness penalty.
My test input data is with n = 9 and m = 3:
jobs_data = [[[(304776, 0, 'r2500', True, 2130458),
(-1, 1, 'r2500', False, 2130458),
(-1, 2, 'r2500', False, 2130458)]],
[[(-1, 0, 'r1200', False, 1295928),
(193491, 1, 'r1200', True, 1295928),
(-1, 2, 'r1200', False, 1295928)]],
[[(215173, 0, 'v3750', False, 1428522),
(-1, 1, 'v3750', False, 1428522),
(-1, 2, 'v3750', False, 1428522)]],
[[(20226, 0, 'v3000', False, 369014),
(20226, 1, 'v3000', False, 369014),
(-1, 2, 'v3000', False, 369014)]],
[[(222962, 0, 'r2500', False, 6517387),
(222962, 1, 'r2500', False, 6517387),
(-1, 2, 'r2500', False, 6517387)]],
[[(-1, 0, 'r1700', False, 1266091),
(-1, 1, 'r1700', False, 1266091),
(80988, 2, 'r1700', False, 1266091)]],
[[(-1, 0, 'r1350', False, 5138051),
(-1, 1, 'r1350', False, 5138051),
(89880, 2, 'r1350', False, 5138051)]],
[[(-1, 0, 'r1200', False, 5699494),
(131899, 1, 'r1200', False, 5699494),
(-1, 2, 'r1200', False, 5699494)]],
[[(-1, 0, 'r1200', False, 505015),
(-1, 1, 'r1200', False, 505015),
(126922, 2, 'r1200', True, 505015)]]]
with a list of n jobs containing m alternative tasks with [processing time, machine_id, resource/material, job already active on machine?, deadline/due date].
Now I created the variable l_lateness. lateness_seconds at the moment is the difference between the scheduled end_time and the deadline.
l_lateness = model.NewIntVar(-horizon_due_date, horizon_due_date, 'l_lateness')
lateness_seconds = (l_end - job[0][0][4])
model.Add(l_lateness == lateness_seconds)
termin.append(l_lateness)
I then use the variable in the objective as follows:
# Objective.
makespan = model.NewIntVar(0, horizon, 'makespan')
model.AddMaxEquality(makespan, job_ends)
makespan_weight = 1
transition_weight = 3
deadline_weight = 5
print(type(sum(termin)))
print(type(sum(switch_literals)))
model.Minimize(makespan * makespan_weight +
sum(switch_literals) * transition_weight +
sum(termin) * deadline_weight)
This works fine. But I want
lateness_seconds = max(0,(l_end - job[0][0][4]))
so that only the jobs where the deadline wasn't met count.
If I do this, then I get the following error message and I don't understand why or how to avoid it:
NotImplementedError: Evaluating a BoundedLinearExpr as a Boolean value is not supported.
I tried to rewrite it as
if l_end >= job[0][0][4]:
model.Add(ueberzogen == (l_end - job[0][0][4]))
else:
model.Add(ueberzogen == 0)
But that gives me the same error for the expression l_end >= job[0][0][4].
I am using ortools Version: 9.0.9048.
We have added pedantic tests in version 9.0 because using min(), max() and other python constructs will not produce a valid model.
Please use AddMinEquality() or AddMaxEquality().