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improve jobshop_sat: fix bug in transitions; remove search heuristics as default is better

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Laurent Perron
2021-07-15 15:36:11 +02:00
parent e877fdde7b
commit 0896548a8e
1 changed files with 27 additions and 15 deletions
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42
examples/cpp/jobshop_sat.cc
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@@ -100,7 +100,6 @@ int64_t ComputeHorizon(const JsspInputProblem& problem) {
}
return std::min(max_latest_end,
sum_of_durations + sum_of_transitions + max_earliest_start);
// TODO(user): Uses transitions.
}
// A job is a sequence of tasks. For each task, we store the main interval, as
@@ -352,17 +351,25 @@ void CreateMachines(
}
// Add transition times if needed.
//
// TODO(user): If there is just a few non-zero transition, there is probably
// a better way than this quadratic blowup.
int64_t num_non_zero_transitions = 0;
for (int m = 0; m < num_machines; ++m) {
if (problem.machines(m).has_transition_time_matrix()) {
const int num_intervals = machine_to_tasks[m].size();
const TransitionTimeMatrix& transitions =
problem.machines(m).transition_time_matrix();
// Create circuit constraint on a machine.
// Node 0 and num_intervals + 1 are source and sink.
// Create circuit constraint on a machine. Node 0 is both the source and
// sink, i.e, the first and last job.
CircuitConstraint circuit = cp_model.AddCircuitConstraint();
for (int i = 0; i < num_intervals; ++i) {
const int job_i = machine_to_tasks[m][i].job;
// TODO(user): simplify the code!
CHECK_EQ(i, job_i);
// Source to nodes.
circuit.AddArc(0, i + 1, cp_model.NewBoolVar());
// Node to sink.
@@ -372,14 +379,19 @@ void CreateMachines(
if (i == j) {
circuit.AddArc(i + 1, i + 1, Not(machine_to_tasks[m][i].presence));
} else {
const int job_j = machine_to_tasks[m][i].job;
const int job_j = machine_to_tasks[m][j].job;
// TODO(user): simplify the code!
CHECK_EQ(j, job_j);
const int64_t transition =
transitions.transition_time(job_i * num_jobs + job_j);
const BoolVar lit = cp_model.NewBoolVar();
const IntVar start = machine_to_tasks[m][j].start;
const IntVar end = machine_to_tasks[m][i].end;
circuit.AddArc(i + 1, j + 1, lit);
// Push the new start with an extra transition.
if (transition != 0) ++num_non_zero_transitions;
cp_model
.AddLessOrEqual(LinearExpr(end).AddConstant(transition), start)
.OnlyEnforceIf(lit);
@@ -388,6 +400,9 @@ void CreateMachines(
}
}
}
if (num_non_zero_transitions > 0) {
LOG(INFO) << "Num non-zeros transition delay: " << num_non_zero_transitions;
}
}
// Collect all objective terms and add them to the model.
@@ -657,15 +672,7 @@ void Solve(const JsspInputProblem& problem) {
makespan, cp_model);
// Decision strategy.
std::vector<IntVar> all_task_starts;
for (const std::vector<JobTaskData>& job : job_to_tasks) {
for (const JobTaskData& task : job) {
all_task_starts.push_back(task.start);
}
}
cp_model.AddDecisionStrategy(all_task_starts,
DecisionStrategyProto::CHOOSE_LOWEST_MIN,
DecisionStrategyProto::SELECT_MIN_VALUE);
// CP-SAT now has a default strategy for scheduling problem that works best.
// Display problem statistics.
int num_tasks = 0;
@@ -736,10 +743,15 @@ void Solve(const JsspInputProblem& problem) {
}
}
// Note that this the objective is a variable of the model, there is actually
// no strong guarantee that in an intermediate solution, it is packed to its
// minimum possible value. We do observe this from time to time. The DCHECK is
// mainly to warn when this happen.
//
// TODO(user): Support alternative cost in check.
const double tolerance = 1e-6;
CHECK_GE(response.objective_value(), final_cost - tolerance);
CHECK_LE(response.objective_value(), final_cost + tolerance);
DCHECK_GE(response.objective_value(), final_cost - tolerance);
DCHECK_LE(response.objective_value(), final_cost + tolerance);
}
} // namespace sat