227 lines
9.5 KiB
C++
227 lines
9.5 KiB
C++
// Copyright 2010-2017 Google
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
//
|
|
// This model implements a simple jobshop problem.
|
|
//
|
|
// A jobshop is a standard scheduling problem where you must schedule a
|
|
// set of jobs on a set of machines. Each job is a sequence of tasks
|
|
// (a task can only start when the preceding task finished), each of
|
|
// which occupies a single specific machine during a specific
|
|
// duration. Therefore, a job is simply given by a sequence of pairs
|
|
// (machine id, duration).
|
|
|
|
// The objective is to minimize the 'makespan', which is the duration
|
|
// between the start of the first task (across all machines) and the
|
|
// completion of the last task (across all machines).
|
|
//
|
|
// This will be modelled by sets of intervals variables (see class
|
|
// IntervalVar in constraint_solver/constraint_solver.h), one per
|
|
// task, representing the [start_time, end_time] of the task. Tasks
|
|
// in the same job will be linked by precedence constraints. Tasks on
|
|
// the same machine will be covered by Sequence constraints.
|
|
//
|
|
// Search will be implemented as local search on the sequence variables.
|
|
|
|
#include "examples/cpp/jobshop_ls.h"
|
|
#include <cstdio>
|
|
#include <cstdlib>
|
|
|
|
#include "examples/cpp/jobshop.h"
|
|
#include "ortools/base/bitmap.h"
|
|
#include "ortools/base/commandlineflags.h"
|
|
#include "ortools/base/integral_types.h"
|
|
#include "ortools/base/logging.h"
|
|
#include "ortools/base/stringprintf.h"
|
|
#include "ortools/constraint_solver/constraint_solver.h"
|
|
#include "ortools/constraint_solver/constraint_solveri.h"
|
|
|
|
DEFINE_string(
|
|
data_file, "",
|
|
"Required: input file description the scheduling problem to solve, "
|
|
"in our jssp format:\n"
|
|
" - the first line is \"instance <instance name>\"\n"
|
|
" - the second line is \"<number of jobs> <number of machines>\"\n"
|
|
" - then one line per job, with a single space-separated "
|
|
"list of \"<machine index> <duration>\"\n"
|
|
"note: jobs with one task are not supported");
|
|
DEFINE_int32(time_limit_in_ms, 60000, "Time limit in ms, 0 means no limit.");
|
|
DEFINE_int32(shuffle_length, 4, "Length of sub-sequences to shuffle LS.");
|
|
DEFINE_int32(sub_sequence_length, 4,
|
|
"Length of sub-sequences to relax in LNS.");
|
|
DEFINE_int32(lns_seed, 1, "Seed of the LNS random search");
|
|
DEFINE_int32(lns_limit, 30,
|
|
"Limit the size of the search tree in a LNS fragment");
|
|
|
|
namespace operations_research {
|
|
// ----- Model and Solve -----
|
|
|
|
void JobshopLs(const JobShopData& data) {
|
|
Solver solver("jobshop");
|
|
const int machine_count = data.machine_count();
|
|
const int job_count = data.job_count();
|
|
const int horizon = data.horizon();
|
|
|
|
// ----- Creates all Intervals and vars -----
|
|
|
|
// Stores all tasks attached interval variables per job.
|
|
std::vector<std::vector<IntervalVar*> > jobs_to_tasks(job_count);
|
|
// machines_to_tasks stores the same interval variables as above, but
|
|
// grouped my machines instead of grouped by jobs.
|
|
std::vector<std::vector<IntervalVar*> > machines_to_tasks(machine_count);
|
|
|
|
// Creates all individual interval variables.
|
|
for (int job_id = 0; job_id < job_count; ++job_id) {
|
|
const std::vector<JobShopData::Task>& tasks = data.TasksOfJob(job_id);
|
|
for (int task_index = 0; task_index < tasks.size(); ++task_index) {
|
|
const JobShopData::Task& task = tasks[task_index];
|
|
CHECK_EQ(job_id, task.job_id);
|
|
const std::string name =
|
|
StringPrintf("J%dM%dI%dD%d", task.job_id, task.machine_id, task_index,
|
|
task.duration);
|
|
IntervalVar* const one_task = solver.MakeFixedDurationIntervalVar(
|
|
0, horizon, task.duration, false, name);
|
|
jobs_to_tasks[task.job_id].push_back(one_task);
|
|
machines_to_tasks[task.machine_id].push_back(one_task);
|
|
}
|
|
}
|
|
|
|
// ----- Creates model -----
|
|
|
|
// Creates precedences inside jobs.
|
|
for (int job_id = 0; job_id < job_count; ++job_id) {
|
|
const int task_count = jobs_to_tasks[job_id].size();
|
|
for (int task_index = 0; task_index < task_count - 1; ++task_index) {
|
|
IntervalVar* const t1 = jobs_to_tasks[job_id][task_index];
|
|
IntervalVar* const t2 = jobs_to_tasks[job_id][task_index + 1];
|
|
Constraint* const prec =
|
|
solver.MakeIntervalVarRelation(t2, Solver::STARTS_AFTER_END, t1);
|
|
solver.AddConstraint(prec);
|
|
}
|
|
}
|
|
|
|
// Adds disjunctive constraints on unary resources, and creates
|
|
// sequence variables. A sequence variable is a dedicated variable
|
|
// whose job is to sequence interval variables.
|
|
std::vector<SequenceVar*> all_sequences;
|
|
for (int machine_id = 0; machine_id < machine_count; ++machine_id) {
|
|
const std::string name = StringPrintf("Machine_%d", machine_id);
|
|
DisjunctiveConstraint* const ct =
|
|
solver.MakeDisjunctiveConstraint(machines_to_tasks[machine_id], name);
|
|
solver.AddConstraint(ct);
|
|
all_sequences.push_back(ct->MakeSequenceVar());
|
|
}
|
|
|
|
// Creates array of end_times of jobs.
|
|
std::vector<IntVar*> all_ends;
|
|
for (int job_id = 0; job_id < job_count; ++job_id) {
|
|
const int task_count = jobs_to_tasks[job_id].size();
|
|
IntervalVar* const task = jobs_to_tasks[job_id][task_count - 1];
|
|
all_ends.push_back(task->EndExpr()->Var());
|
|
}
|
|
|
|
// Objective: minimize the makespan (maximum end times of all tasks)
|
|
// of the problem.
|
|
IntVar* const objective_var = solver.MakeMax(all_ends)->Var();
|
|
|
|
// ----- Search monitors and decision builder -----
|
|
|
|
// This decision builder will rank all tasks on all machines.
|
|
DecisionBuilder* const sequence_phase =
|
|
solver.MakePhase(all_sequences, Solver::SEQUENCE_DEFAULT);
|
|
|
|
// After the ranking of tasks, the schedule is still loose and any
|
|
// task can be postponed at will. But, because the problem is now a PERT
|
|
// (http://en.wikipedia.org/wiki/Program_Evaluation_and_Review_Technique),
|
|
// we can schedule each task at its earliest start time. This is
|
|
// conveniently done by fixing the objective variable to its
|
|
// minimum value.
|
|
DecisionBuilder* const obj_phase = solver.MakePhase(
|
|
objective_var, Solver::CHOOSE_FIRST_UNBOUND, Solver::ASSIGN_MIN_VALUE);
|
|
|
|
Assignment* const first_solution = solver.MakeAssignment();
|
|
first_solution->Add(all_sequences);
|
|
first_solution->AddObjective(objective_var);
|
|
// Store the first solution in the 'solution' object.
|
|
DecisionBuilder* const store_db = solver.MakeStoreAssignment(first_solution);
|
|
|
|
// The main decision builder (ranks all tasks, then fixes the
|
|
// objective_variable).
|
|
DecisionBuilder* const first_solution_phase =
|
|
solver.Compose(sequence_phase, obj_phase, store_db);
|
|
|
|
LOG(INFO) << "Looking for the first solution and improving with local search";
|
|
std::vector<LocalSearchOperator*> operators;
|
|
LOG(INFO) << " - use swap operator";
|
|
LocalSearchOperator* const swap_operator =
|
|
solver.RevAlloc(new SwapIntervals(all_sequences));
|
|
operators.push_back(swap_operator);
|
|
LOG(INFO) << " - use shuffle operator with a max length of "
|
|
<< FLAGS_shuffle_length;
|
|
LocalSearchOperator* const shuffle_operator = solver.RevAlloc(
|
|
new ShuffleIntervals(all_sequences, FLAGS_shuffle_length));
|
|
operators.push_back(shuffle_operator);
|
|
LOG(INFO) << " - use free sub sequences of length "
|
|
<< FLAGS_sub_sequence_length << " lns operator";
|
|
LocalSearchOperator* const lns_operator = solver.RevAlloc(new SequenceLns(
|
|
all_sequences, FLAGS_lns_seed, FLAGS_sub_sequence_length));
|
|
operators.push_back(lns_operator);
|
|
|
|
// Creates the local search decision builder.
|
|
LocalSearchOperator* const concat =
|
|
solver.ConcatenateOperators(operators, true);
|
|
|
|
SearchLimit* const ls_limit =
|
|
solver.MakeLimit(kint64max, FLAGS_lns_limit, kint64max, kint64max);
|
|
DecisionBuilder* const random_sequence_phase =
|
|
solver.MakePhase(all_sequences, Solver::CHOOSE_RANDOM_RANK_FORWARD);
|
|
DecisionBuilder* const ls_db = solver.MakeSolveOnce(
|
|
solver.Compose(random_sequence_phase, obj_phase), ls_limit);
|
|
|
|
LocalSearchPhaseParameters* const parameters =
|
|
solver.MakeLocalSearchPhaseParameters(concat, ls_db);
|
|
DecisionBuilder* const final_db = solver.MakeLocalSearchPhase(
|
|
all_sequences, first_solution_phase, parameters);
|
|
|
|
OptimizeVar* const objective_monitor = solver.MakeMinimize(objective_var, 1);
|
|
|
|
// Search log.
|
|
const int kLogFrequency = 1000000;
|
|
SearchMonitor* const search_log =
|
|
solver.MakeSearchLog(kLogFrequency, objective_monitor);
|
|
|
|
SearchLimit* const limit = FLAGS_time_limit_in_ms > 0
|
|
? solver.MakeTimeLimit(FLAGS_time_limit_in_ms)
|
|
: nullptr;
|
|
|
|
// Search.
|
|
solver.Solve(final_db, search_log, objective_monitor, limit);
|
|
}
|
|
} // namespace operations_research
|
|
|
|
static const char kUsage[] =
|
|
"Usage: see flags.\nThis program runs a simple job shop optimization "
|
|
"output besides the debug LOGs of the solver.";
|
|
|
|
int main(int argc, char** argv) {
|
|
gflags::SetUsageMessage(kUsage);
|
|
gflags::ParseCommandLineFlags(&argc, &argv, true);
|
|
if (FLAGS_data_file.empty()) {
|
|
LOG(FATAL) << "Please supply a data file with --data_file=";
|
|
}
|
|
operations_research::JobShopData data;
|
|
data.Load(FLAGS_data_file);
|
|
operations_research::JobshopLs(data);
|
|
return EXIT_SUCCESS;
|
|
}
|