6.2. An implementation of the disjunctive model

  1. C++ code:
    1. jobshop.cc
  2. Data files:
    1. first_example_jssp.txt
    2. abz9

Scheduling is one of the fields where Constraint Programming is heavily used and where specialized constraints and variables have been developed[1]. In this section, we will implement the disjunctive model with dedicated variables (IntervalVar and SequenceVar) and constraints (IntervalBinaryRelation and DisjunctiveConstraint).

Last but not least, we will see our first real example of combining two DecisionBuilders in a top-down fashion.

6.2.1. The IntervalVar variables

We create one IntervalVar for each task. Remember the Task struct we use in the JobShopData class:

struct Task {
  Task(int j, int m, int d) : job_id(j), machine_id(m), duration(d) {}
  int job_id;
  int machine_id;
  int duration;

An IntervalVar represents one integer interval and is often used in scheduling. Its main characteristics are its starting time, its duration and its ending time.

The CP solver has the factory method MakeFixedDurationIntervalVar() for fixed duration intervals:

const std::string name = StringPrintf("J%dM%dI%dD%d",
IntervalVar* const one_task =

The first two arguments of MakeFixedDurationIntervalVar() are a lower and an upper bound on the starting time of the IntervalVar. The fourth argument is a bool that indicates if the IntervalVar can be unperformed or not. Unperformed IntervalVars simply don’t exist anymore. This can happen when the IntervalVar is not consistent anymore. By setting the argument to false, we don’t allow this variable to be unperformed.

To be able to easily retrieve the tasks corresponding to a job or a machine, we use two matrices:

std::vector<std::vector<IntervalVar*> > jobs_to_tasks(job_count);
std::vector<std::vector<IntervalVar*> >

and populate them:

// 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 string name = ...
    IntervalVar* const one_task = ...

We will create the SequenceVar variables later when we will add the disjunctive constraints.

6.2.2. The conjunctive constraints

Recall that the conjunctive constraints ensure the sequence order of tasks inside a job is respected. If IntervalVar t1 is the task right before IntervalVar t2 in a job, we can add an IntervalBinaryRelation constraint with the right relation between the two IntervalVars. In this case, the relation is STARTS_AFTER_END:

Constraint* const prec =
   solver.MakeIntervalVarRelation(t2, Solver::STARTS_AFTER_END, t1);

In the next section, we will examine other possibilities and also temporal relations between an IntervalVar t and an integer d representing time.

6.2.3. The disjunctive constraints and SequenceVars

The disjunctive constraints ensure that the tasks are correctly processed on each machine, i.e. a task is processed entirely before or after another task on a single machine. The CP solver provides DisjunctiveConstraints and a corresponding factory method:

const std::string name = StringPrintf("Machine_%d", machine_id);
DisjunctiveConstraint* const ct =

A SequenceVar variable is a variable whose domain is a set of possible orderings of the IntervalVars. It allows ordering tasks.

You can only create[2] SequenceVars with the MakeSequenceVar() method of the DisjunctiveConstraint class:

std::vector<SequenceVar*> all_sequences;
for (int machine_id = 0; machine_id < machine_count; ++machine_id) {
  const string name = StringPrintf("Machine_%d", machine_id);
  DisjunctiveConstraint* const ct =
  solver.MakeDisjunctiveConstraint(machines_to_tasks[machine_id], name);

6.2.4. The objective function

To create the makespan variable, we simply collect the last tasks of all the jobs and store the maximum of their end times:

// 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];

// Objective: minimize the makespan (maximum end times of all tasks)
// of the problem.
IntVar* const objective_var = solver.MakeMax(all_ends)->Var();
OptimizeVar* const objective_monitor =
                              solver.MakeMinimize(objective_var, 1);

To obtain the end time of an IntervalVar, use its EndExpr() method that returns an IntExpr. You can also query the start time and duration:

  • StartExpr();
  • DurationExpr().

6.2.5. The DecisionBuilders

The solving process is done in two sequential phases: first we rank the tasks for each machine, then we schedule each task at its earliest start time. This is done with two DecisionBuilders that are combined in a top-down fashion, i.e. one DecisionBuilder is applied and then when we reach a leaf in its search tree, the second DecisionBuilder kicks in. Since this chapter is about local search, we will use default search strategies for both phases.

First, we define the phase to rank the tasks on all machines:

DecisionBuilder* const sequence_phase =
          solver.MakePhase(all_sequences, Solver::SEQUENCE_DEFAULT);

Second, we define the phase to schedule the ranked tasks. This is conveniently done by fixing the objective variable to its minimum value:

DecisionBuilder* const obj_phase = solver.MakePhase(objective_var,

Third, we combine both phases one after the other in the search tree with the Compose() method:

DecisionBuilder* const main_phase =
                         solver.Compose(sequence_phase, obj_phase);

6.2.6. The search and first results

We use the usual SearchMonitors:

// Search log.
const int kLogFrequency = 1000000;
SearchMonitor* const search_log =
            solver.MakeSearchLog(kLogFrequency, objective_monitor);

SearchLimit* limit = NULL;
if (FLAGS_time_limit_in_ms > 0) {
  limit = solver.MakeTimeLimit(FLAGS_time_limit_in_ms);

SolutionCollector* const collector =

and launch the search:

// Search.
if (solver.Solve(main_phase,
                 collector)) {
  for (int m = 0; m < machine_count; ++m) {
    LOG(INFO) << "Objective value: " <<
    SequenceVar* const seq = all_sequences[m];
    LOG(INFO) << seq->name() << ": "
    << IntVectorToString(collector->ForwardSequence(0, seq), ", ");

collector->ForwardSequence(0, seq) is a shortcut to return the std::vector<int> containing the order in which the tasks are processed on each machine for solution 0 (which is the last and thus optimal solution).

This order corresponds exactly to the job ids because the tasks are sorted by job id on each machine. The result for our instance is:

[09:21:44] jobshop.cc:150: Machine_0: 0, 1
[09:21:44] jobshop.cc:150: Machine_1: 2, 0, 1
[09:21:44] jobshop.cc:150: Machine_2: 1, 0, 2

which is exactly the optimal solution depicted in the previous section.

What about getting the start and end times for all tasks?

Declare the corresponding variables in the SolutionCollector:

SolutionCollector* const collector =

for (int seq = 0; seq < all_sequences.size(); ++seq) {
  const SequenceVar * sequence = all_sequences[seq];
  const int sequence_count = sequence->size();
  for (int i = 0; i < sequence_count; ++i) {
    IntervalVar * t = sequence->Interval(i);

and then print the desired information:

for (int m = 0; m < machine_count; ++m) {
  SequenceVar* const seq = all_sequences[m];
  std::ostringstream s;
  s << seq->name() << ": ";
  const std::vector<int> & sequence =
                                collector->ForwardSequence(0, seq);
  const int seq_size = sequence.size();
  for (int i = 0; i < seq_size; ++i) {
    IntervalVar * t = seq->Interval(sequence[i]);
    s << "Job " << sequence[i] << " (";
    s << collector->Value(0,t->StartExpr()->Var());
    s << ",";
    s << collector->Value(0,t->EndExpr()->Var());
    s << ")  ";
  LOG(INFO) << s.str();

The result for our instance is:

...: Machine_0: Job 0 (0,3)  Job 1 (3,5)
...: Machine_1: Job 2 (0,4)  Job 0 (4,6)  Job 1 (6,10)
...: Machine_2: Job 1 (5,6)  Job 0 (6,8)  Job 2 (8,11)

Let’s try the abz9 instance:

Sol. nbr. Obj. val. Branches Time (s)
87 1015 131 733 26,756
107 986 6 242 194 1088,487

After a little bit more than 18 minutes (1088,487 seconds), the CP solver finds its 107 th solution with an objective value of 986. This is quite far from the optimal value of... 679 [Adams1988]. An exact procedure to solve the job-shop problem is possible but only for small instances and with specialized algorithms.

We prefer to quickly find (hopefully) good solutions (see the section The Job-Shop Problem: and now with Local Search!).

We will discover next what specialized tools are available in our library to handle scheduling problems.


[1]The next section is entirely dedicated to scheduling in or-tools.
[2]The factory method Solver::MakeSequenceVar(...) has been removed from the API.


[Adams1988]J. Adams, E. Balas, D. Zawack, The shifting bottleneck procedure for job shop scheduling. Management Science, 34, pp 391-401, 1988.