resource.cc

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// Copyright 2010-2018 Google LLC
// 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 file contains implementations of several resource constraints.
// The implemented constraints are:
// * Disjunctive: forces a set of intervals to be non-overlapping
// * Cumulative: forces a set of intervals with associated demands to be such
//     that the sum of demands of the intervals containing any given integer
//     does not exceed a capacity.
// In addition, it implements the SequenceVar that allows ranking decisions
// on a set of interval variables.
 
#include <algorithm>
#include <queue>
#include <string>
#include <utility>
#include <vector>
 
#include "absl/container/flat_hash_map.h"
#include "absl/strings/str_cat.h"
#include "absl/strings/str_format.h"
#include "absl/strings/str_join.h"
#include "ortools/base/commandlineflags.h"
#include "ortools/base/integral_types.h"
#include "ortools/base/logging.h"
#include "ortools/base/macros.h"
#include "ortools/base/mathutil.h"
#include "ortools/base/stl_util.h"
#include "ortools/constraint_solver/constraint_solver.h"
#include "ortools/constraint_solver/constraint_solveri.h"
#include "ortools/util/bitset.h"
#include "ortools/util/monoid_operation_tree.h"
#include "ortools/util/saturated_arithmetic.h"
#include "ortools/util/string_array.h"
 
namespace operations_research {
namespace {
// ----- Comparison functions -----
 
// TODO(user): Tie breaking.
 
// Comparison methods, used by the STL sort.
template <class Task>
bool StartMinLessThan(Task* const w1, Task* const w2) {
  return (w1->interval->StartMin() < w2->interval->StartMin());
}
 
// A comparator that sorts the tasks by their effective earliest start time when
// using the shortest duration possible. This comparator can be used when
// sorting the tasks before they are inserted to a Theta-tree.
template <class Task>
bool ShortestDurationStartMinLessThan(Task* const w1, Task* const w2) {
  return w1->interval->EndMin() - w1->interval->DurationMin() <
         w2->interval->EndMin() - w2->interval->DurationMin();
}
 
template <class Task>
bool StartMaxLessThan(Task* const w1, Task* const w2) {
  return (w1->interval->StartMax() < w2->interval->StartMax());
}
 
template <class Task>
bool EndMinLessThan(Task* const w1, Task* const w2) {
  return (w1->interval->EndMin() < w2->interval->EndMin());
}
 
template <class Task>
bool EndMaxLessThan(Task* const w1, Task* const w2) {
  return (w1->interval->EndMax() < w2->interval->EndMax());
}
 
bool IntervalStartMinLessThan(IntervalVar* i1, IntervalVar* i2) {
  return i1->StartMin() < i2->StartMin();
}
 
// ----- Wrappers around intervals -----
 
// A DisjunctiveTask is a non-preemptive task sharing a disjunctive resource.
// That is, it corresponds to an interval, and this interval cannot overlap with
// any other interval of a DisjunctiveTask sharing the same resource.
// It is indexed, that is it is aware of its position in a reference array.
struct DisjunctiveTask {
  explicit DisjunctiveTask(IntervalVar* const interval_)
      : interval(interval_), index(-1) {}
 
  std::string DebugString() const { return interval->DebugString(); }
 
  IntervalVar* interval;
  int index;
};
 
// A CumulativeTask is a non-preemptive task sharing a cumulative resource.
// That is, it corresponds to an interval and a demand. The sum of demands of
// all cumulative tasks CumulativeTasks sharing a resource of capacity c those
// intervals contain any integer t cannot exceed c.
// It is indexed, that is it is aware of its position in a reference array.
struct CumulativeTask {
  CumulativeTask(IntervalVar* const interval_, int64 demand_)
      : interval(interval_), demand(demand_), index(-1) {}
 
  int64 EnergyMin() const { return interval->DurationMin() * demand; }
 
  int64 DemandMin() const { return demand; }
 
  void WhenAnything(Demon* const demon) { interval->WhenAnything(demon); }
 
  std::string DebugString() const {
    return absl::StrFormat("Task{ %s, demand: %d }", interval->DebugString(),
                           demand);
  }
 
  IntervalVar* interval;
  int64 demand;
  int index;
};
 
// A VariableCumulativeTask is a non-preemptive task sharing a
// cumulative resource.  That is, it corresponds to an interval and a
// demand. The sum of demands of all cumulative tasks
// VariableCumulativeTasks sharing a resource of capacity c whose
// intervals contain any integer t cannot exceed c.  It is indexed,
// that is it is aware of its position in a reference array.
struct VariableCumulativeTask {
  VariableCumulativeTask(IntervalVar* const interval_, IntVar* demand_)
      : interval(interval_), demand(demand_), index(-1) {}
 
  int64 EnergyMin() const { return interval->DurationMin() * demand->Min(); }
 
  int64 DemandMin() const { return demand->Min(); }
 
  void WhenAnything(Demon* const demon) {
    interval->WhenAnything(demon);
    demand->WhenRange(demon);
  }
 
  std::string DebugString() const {
    return absl::StrFormat("Task{ %s, demand: %s }", interval->DebugString(),
                           demand->DebugString());
  }
 
  IntervalVar* const interval;
  IntVar* const demand;
  int index;
};
 
// ---------- Theta-Trees ----------
 
// This is based on Petr Vilim (public) PhD work.
// All names comes from his work. See http://vilim.eu/petr.
 
// Node of a Theta-tree
struct ThetaNode {
  // Identity element
  ThetaNode() : total_processing(0), total_ect(kint64min) {}
 
  // Single interval element
  explicit ThetaNode(const IntervalVar* const interval)
      : total_processing(interval->DurationMin()),
        total_ect(interval->EndMin()) {
    // NOTE(user): Petr Vilim's thesis assumes that all tasks in the
    // scheduling problem have fixed duration and that propagation already
    // updated the bounds of the start/end times accordingly.
    // The problem in this case is that the recursive formula for computing
    // total_ect was only proved for the case where the duration is fixed; in
    // our case, we use StartMin() + DurationMin() for the earliest completion
    // time of a task, which should not break any assumptions, but may give
    // bounds that are too loose.
  }
 
  void Compute(const ThetaNode& left, const ThetaNode& right) {
    total_processing = CapAdd(left.total_processing, right.total_processing);
    total_ect = std::max(CapAdd(left.total_ect, right.total_processing),
                         right.total_ect);
  }
 
  bool IsIdentity() const {
    return total_processing == 0LL && total_ect == kint64min;
  }
 
  std::string DebugString() const {
    return absl::StrCat("ThetaNode{ p = ", total_processing,
                        ", e = ", total_ect < 0LL ? -1LL : total_ect, " }");
  }
 
  int64 total_processing;
  int64 total_ect;
};
 
// A theta-tree is a container for a set of intervals supporting the following
// operations:
// * Insertions and deletion in O(log size_), with size_ the maximal number of
//     tasks the tree may contain;
// * Querying the following quantity in O(1):
//     Max_{subset S of the set of contained intervals} (
//             Min_{i in S}(i.StartMin) + Sum_{i in S}(i.DurationMin) )
class ThetaTree : public MonoidOperationTree<ThetaNode> {
 public:
  explicit ThetaTree(int size) : MonoidOperationTree<ThetaNode>(size) {}
 
  int64 Ect() const { return result().total_ect; }
 
  void Insert(const DisjunctiveTask* const task) {
    Set(task->index, ThetaNode(task->interval));
  }
 
  void Remove(const DisjunctiveTask* const task) { Reset(task->index); }
 
  bool IsInserted(const DisjunctiveTask* const task) const {
    return !GetOperand(task->index).IsIdentity();
  }
};
 
// ----------------- Lambda Theta Tree -----------------------
 
// Lambda-theta-node
// These nodes are cumulative lambda theta-node. This is reflected in the
// terminology. They can also be used in the disjunctive case, and this incurs
// no performance penalty.
struct LambdaThetaNode {
  // Special value for task indices meaning 'no such task'.
  static const int kNone;
 
  // Identity constructor
  LambdaThetaNode()
      : energy(0LL),
        energetic_end_min(kint64min),
        energy_opt(0LL),
        argmax_energy_opt(kNone),
        energetic_end_min_opt(kint64min),
        argmax_energetic_end_min_opt(kNone) {}
 
  // Constructor for a single cumulative task in the Theta set
  LambdaThetaNode(int64 capacity, const CumulativeTask& task)
      : energy(task.EnergyMin()),
        energetic_end_min(CapAdd(capacity * task.interval->StartMin(), energy)),
        energy_opt(energy),
        argmax_energy_opt(kNone),
        energetic_end_min_opt(energetic_end_min),
        argmax_energetic_end_min_opt(kNone) {}
 
  // Constructor for a single cumulative task in the Lambda set
  LambdaThetaNode(int64 capacity, const CumulativeTask& task, int index)
      : energy(0LL),
        energetic_end_min(kint64min),
        energy_opt(task.EnergyMin()),
        argmax_energy_opt(index),
        energetic_end_min_opt(capacity * task.interval->StartMin() +
                              energy_opt),
        argmax_energetic_end_min_opt(index) {
    DCHECK_GE(index, 0);
  }
 
  // Constructor for a single cumulative task in the Theta set
  LambdaThetaNode(int64 capacity, const VariableCumulativeTask& task)
      : energy(task.EnergyMin()),
        energetic_end_min(CapAdd(capacity * task.interval->StartMin(), energy)),
        energy_opt(energy),
        argmax_energy_opt(kNone),
        energetic_end_min_opt(energetic_end_min),
        argmax_energetic_end_min_opt(kNone) {}
 
  // Constructor for a single cumulative task in the Lambda set
  LambdaThetaNode(int64 capacity, const VariableCumulativeTask& task, int index)
      : energy(0LL),
        energetic_end_min(kint64min),
        energy_opt(task.EnergyMin()),
        argmax_energy_opt(index),
        energetic_end_min_opt(capacity * task.interval->StartMin() +
                              energy_opt),
        argmax_energetic_end_min_opt(index) {
    DCHECK_GE(index, 0);
  }
 
  // Constructor for a single interval in the Theta set
  explicit LambdaThetaNode(const IntervalVar* const interval)
      : energy(interval->DurationMin()),
        energetic_end_min(interval->EndMin()),
        energy_opt(interval->DurationMin()),
        argmax_energy_opt(kNone),
        energetic_end_min_opt(interval->EndMin()),
        argmax_energetic_end_min_opt(kNone) {}
 
  // Constructor for a single interval in the Lambda set
  // 'index' is the index of the given interval in the est vector
  LambdaThetaNode(const IntervalVar* const interval, int index)
      : energy(0LL),
        energetic_end_min(kint64min),
        energy_opt(interval->DurationMin()),
        argmax_energy_opt(index),
        energetic_end_min_opt(interval->EndMin()),
        argmax_energetic_end_min_opt(index) {
    DCHECK_GE(index, 0);
  }
 
  // Sets this LambdaThetaNode to the result of the natural binary operations
  // over the two given operands, corresponding to the following set operations:
  // Theta = left.Theta union right.Theta
  // Lambda = left.Lambda union right.Lambda
  //
  // No set operation actually occur: we only maintain the relevant quantities
  // associated with such sets.
  void Compute(const LambdaThetaNode& left, const LambdaThetaNode& right) {
    energy = CapAdd(left.energy, right.energy);
    energetic_end_min = std::max(right.energetic_end_min,
                                 CapAdd(left.energetic_end_min, right.energy));
    const int64 energy_left_opt = CapAdd(left.energy_opt, right.energy);
    const int64 energy_right_opt = CapAdd(left.energy, right.energy_opt);
    if (energy_left_opt > energy_right_opt) {
      energy_opt = energy_left_opt;
      argmax_energy_opt = left.argmax_energy_opt;
    } else {
      energy_opt = energy_right_opt;
      argmax_energy_opt = right.argmax_energy_opt;
    }
    const int64 ect1 = right.energetic_end_min_opt;
    const int64 ect2 = CapAdd(left.energetic_end_min, right.energy_opt);
    const int64 ect3 = CapAdd(left.energetic_end_min_opt, right.energy);
    if (ect1 >= ect2 && ect1 >= ect3) {  // ect1 max
      energetic_end_min_opt = ect1;
      argmax_energetic_end_min_opt = right.argmax_energetic_end_min_opt;
    } else if (ect2 >= ect1 && ect2 >= ect3) {  // ect2 max
      energetic_end_min_opt = ect2;
      argmax_energetic_end_min_opt = right.argmax_energy_opt;
    } else {  // ect3 max
      energetic_end_min_opt = ect3;
      argmax_energetic_end_min_opt = left.argmax_energetic_end_min_opt;
    }
    // The processing time, with one grey interval, should be no less than
    // without any grey interval.
    DCHECK(energy_opt >= energy);
    // If there is no responsible grey interval for the processing time,
    // the processing time with a grey interval should equal the one
    // without.
    DCHECK((argmax_energy_opt != kNone) || (energy_opt == energy));
  }
 
  // Amount of resource consumed by the Theta set, in units of demand X time.
  // This is energy(Theta).
  int64 energy;
 
  // Max_{subset S of Theta} (capacity * start_min(S) + energy(S))
  int64 energetic_end_min;
 
  // Max_{i in Lambda} (energy(Theta union {i}))
  int64 energy_opt;
 
  // The argmax in energy_opt_. It is the index of the chosen task in the Lambda
  // set, if any, or kNone if none.
  int argmax_energy_opt;
 
  // Max_{subset S of Theta, i in Lambda}
  //     (capacity * start_min(S union {i}) + energy(S union {i}))
  int64 energetic_end_min_opt;
 
  // The argmax in energetic_end_min_opt_. It is the index of the chosen task in
  // the Lambda set, if any, or kNone if none.
  int argmax_energetic_end_min_opt;
};
 
const int LambdaThetaNode::kNone = -1;
 
// Disjunctive Lambda-Theta tree
class DisjunctiveLambdaThetaTree : public MonoidOperationTree<LambdaThetaNode> {
 public:
  explicit DisjunctiveLambdaThetaTree(int size)
      : MonoidOperationTree<LambdaThetaNode>(size) {}
 
  void Insert(const DisjunctiveTask& task) {
    Set(task.index, LambdaThetaNode(task.interval));
  }
 
  void Grey(const DisjunctiveTask& task) {
    const int index = task.index;
    Set(index, LambdaThetaNode(task.interval, index));
  }
 
  int64 Ect() const { return result().energetic_end_min; }
  int64 EctOpt() const { return result().energetic_end_min_opt; }
  int ResponsibleOpt() const { return result().argmax_energetic_end_min_opt; }
};
 
// A cumulative lambda-theta tree
class CumulativeLambdaThetaTree : public MonoidOperationTree<LambdaThetaNode> {
 public:
  CumulativeLambdaThetaTree(int size, int64 capacity_max)
      : MonoidOperationTree<LambdaThetaNode>(size),
        capacity_max_(capacity_max) {}
 
  void Init(int64 capacity_max) {
    Clear();
    capacity_max_ = capacity_max;
  }
 
  void Insert(const CumulativeTask& task) {
    Set(task.index, LambdaThetaNode(capacity_max_, task));
  }
 
  void Grey(const CumulativeTask& task) {
    const int index = task.index;
    Set(index, LambdaThetaNode(capacity_max_, task, index));
  }
 
  void Insert(const VariableCumulativeTask& task) {
    Set(task.index, LambdaThetaNode(capacity_max_, task));
  }
 
  void Grey(const VariableCumulativeTask& task) {
    const int index = task.index;
    Set(index, LambdaThetaNode(capacity_max_, task, index));
  }
 
  int64 energetic_end_min() const { return result().energetic_end_min; }
  int64 energetic_end_min_opt() const { return result().energetic_end_min_opt; }
  int64 Ect() const {
    return MathUtil::CeilOfRatio(energetic_end_min(), capacity_max_);
  }
  int64 EctOpt() const {
    return MathUtil::CeilOfRatio(result().energetic_end_min_opt, capacity_max_);
  }
  int argmax_energetic_end_min_opt() const {
    return result().argmax_energetic_end_min_opt;
  }
 
 private:
  int64 capacity_max_;
};
 
// -------------- Not Last -----------------------------------------
 
// A class that implements the 'Not-Last' propagation algorithm for the unary
// resource constraint.
class NotLast {
 public:
  NotLast(Solver* const solver, const std::vector<IntervalVar*>& intervals,
          bool mirror, bool strict);
 
  ~NotLast() { gtl::STLDeleteElements(&by_start_min_); }
 
  bool Propagate();
 
 private:
  ThetaTree theta_tree_;
  std::vector<DisjunctiveTask*> by_start_min_;
  std::vector<DisjunctiveTask*> by_end_max_;
  std::vector<DisjunctiveTask*> by_start_max_;
  std::vector<int64> new_lct_;
  const bool strict_;
};
 
NotLast::NotLast(Solver* const solver,
                 const std::vector<IntervalVar*>& intervals, bool mirror,
                 bool strict)
    : theta_tree_(intervals.size()),
      by_start_min_(intervals.size()),
      by_end_max_(intervals.size()),
      by_start_max_(intervals.size()),
      new_lct_(intervals.size(), -1LL),
      strict_(strict) {
  // Populate the different vectors.
  for (int i = 0; i < intervals.size(); ++i) {
    IntervalVar* const underlying =
        mirror ? solver->MakeMirrorInterval(intervals[i]) : intervals[i];
    IntervalVar* const relaxed = solver->MakeIntervalRelaxedMin(underlying);
    by_start_min_[i] = new DisjunctiveTask(relaxed);
    by_end_max_[i] = by_start_min_[i];
    by_start_max_[i] = by_start_min_[i];
  }
}
 
bool NotLast::Propagate() {
  // ---- Init ----
  std::sort(by_start_max_.begin(), by_start_max_.end(),
            StartMaxLessThan<DisjunctiveTask>);
  std::sort(by_end_max_.begin(), by_end_max_.end(),
            EndMaxLessThan<DisjunctiveTask>);
  // Update start min positions
  std::sort(by_start_min_.begin(), by_start_min_.end(),
            StartMinLessThan<DisjunctiveTask>);
  for (int i = 0; i < by_start_min_.size(); ++i) {
    by_start_min_[i]->index = i;
  }
  theta_tree_.Clear();
  for (int i = 0; i < by_start_min_.size(); ++i) {
    new_lct_[i] = by_start_min_[i]->interval->EndMax();
  }
 
  // --- Execute ----
  int j = 0;
  for (DisjunctiveTask* const twi : by_end_max_) {
    while (j < by_start_max_.size() &&
           twi->interval->EndMax() > by_start_max_[j]->interval->StartMax()) {
      if (j > 0 && theta_tree_.Ect() > by_start_max_[j]->interval->StartMax()) {
        const int64 new_end_max = by_start_max_[j - 1]->interval->StartMax();
        new_lct_[by_start_max_[j]->index] = new_end_max;
      }
      theta_tree_.Insert(by_start_max_[j]);
      j++;
    }
    const bool inserted = theta_tree_.IsInserted(twi);
    if (inserted) {
      theta_tree_.Remove(twi);
    }
    const int64 ect_theta_less_i = theta_tree_.Ect();
    if (inserted) {
      theta_tree_.Insert(twi);
    }
    if (ect_theta_less_i > twi->interval->EndMax() && j > 0) {
      const int64 new_end_max = by_start_max_[j - 1]->interval->EndMax();
      if (new_end_max > new_lct_[twi->index]) {
        new_lct_[twi->index] = new_end_max;
      }
    }
  }
 
  // Apply modifications
  bool modified = false;
  for (int i = 0; i < by_start_min_.size(); ++i) {
    IntervalVar* const var = by_start_min_[i]->interval;
    if ((strict_ || var->DurationMin() > 0) && var->EndMax() > new_lct_[i]) {
      modified = true;
      var->SetEndMax(new_lct_[i]);
    }
  }
  return modified;
}
 
// ------ Edge finder + detectable precedences -------------
 
// A class that implements two propagation algorithms: edge finding and
// detectable precedences. These algorithms both push intervals to the right,
// which is why they are grouped together.
class EdgeFinderAndDetectablePrecedences {
 public:
  EdgeFinderAndDetectablePrecedences(Solver* const solver,
                                     const std::vector<IntervalVar*>& intervals,
                                     bool mirror, bool strict);
  ~EdgeFinderAndDetectablePrecedences() {
    gtl::STLDeleteElements(&by_start_min_);
  }
  int64 size() const { return by_start_min_.size(); }
  IntervalVar* interval(int index) { return by_start_min_[index]->interval; }
  void UpdateEst();
  void OverloadChecking();
  bool DetectablePrecedences();
  bool EdgeFinder();
 
 private:
  Solver* const solver_;
 
  // --- All the following member variables are essentially used as local ones:
  // no invariant is maintained about them, except for the fact that the vectors
  // always contains all the considered intervals, so any function that wants to
  // use them must first sort them in the right order.
 
  // All of these vectors store the same set of objects. Therefore, at
  // destruction time, STLDeleteElements should be called on only one of them.
  // It does not matter which one.
 
  ThetaTree theta_tree_;
  std::vector<DisjunctiveTask*> by_end_min_;
  std::vector<DisjunctiveTask*> by_start_min_;
  std::vector<DisjunctiveTask*> by_end_max_;
  std::vector<DisjunctiveTask*> by_start_max_;
  // new_est_[i] is the new start min for interval est_[i]->interval.
  std::vector<int64> new_est_;
  // new_lct_[i] is the new end max for interval est_[i]->interval.
  std::vector<int64> new_lct_;
  DisjunctiveLambdaThetaTree lt_tree_;
  const bool strict_;
};
 
EdgeFinderAndDetectablePrecedences::EdgeFinderAndDetectablePrecedences(
    Solver* const solver, const std::vector<IntervalVar*>& intervals,
    bool mirror, bool strict)
    : solver_(solver),
      theta_tree_(intervals.size()),
      lt_tree_(intervals.size()),
      strict_(strict) {
  // Populate of the array of intervals
  for (IntervalVar* const interval : intervals) {
    IntervalVar* const underlying =
        mirror ? solver->MakeMirrorInterval(interval) : interval;
    IntervalVar* const relaxed = solver->MakeIntervalRelaxedMax(underlying);
    DisjunctiveTask* const task = new DisjunctiveTask(relaxed);
    by_end_min_.push_back(task);
    by_start_min_.push_back(task);
    by_end_max_.push_back(task);
    by_start_max_.push_back(task);
    new_est_.push_back(kint64min);
  }
}
 
void EdgeFinderAndDetectablePrecedences::UpdateEst() {
  std::sort(by_start_min_.begin(), by_start_min_.end(),
            ShortestDurationStartMinLessThan<DisjunctiveTask>);
  for (int i = 0; i < size(); ++i) {
    by_start_min_[i]->index = i;
  }
}
 
void EdgeFinderAndDetectablePrecedences::OverloadChecking() {
  // Initialization.
  UpdateEst();
  std::sort(by_end_max_.begin(), by_end_max_.end(),
            EndMaxLessThan<DisjunctiveTask>);
  theta_tree_.Clear();
 
  for (DisjunctiveTask* const task : by_end_max_) {
    theta_tree_.Insert(task);
    if (theta_tree_.Ect() > task->interval->EndMax()) {
      solver_->Fail();
    }
  }
}
 
bool EdgeFinderAndDetectablePrecedences::DetectablePrecedences() {
  // Initialization.
  UpdateEst();
  new_est_.assign(size(), kint64min);
 
  // Propagate in one direction
  std::sort(by_end_min_.begin(), by_end_min_.end(),
            EndMinLessThan<DisjunctiveTask>);
  std::sort(by_start_max_.begin(), by_start_max_.end(),
            StartMaxLessThan<DisjunctiveTask>);
  theta_tree_.Clear();
  int j = 0;
  for (DisjunctiveTask* const task_i : by_end_min_) {
    if (j < size()) {
      DisjunctiveTask* task_j = by_start_max_[j];
      while (task_i->interval->EndMin() > task_j->interval->StartMax()) {
        theta_tree_.Insert(task_j);
        j++;
        if (j == size()) break;
        task_j = by_start_max_[j];
      }
    }
    const int64 esti = task_i->interval->StartMin();
    bool inserted = theta_tree_.IsInserted(task_i);
    if (inserted) {
      theta_tree_.Remove(task_i);
    }
    const int64 oesti = theta_tree_.Ect();
    if (inserted) {
      theta_tree_.Insert(task_i);
    }
    if (oesti > esti) {
      new_est_[task_i->index] = oesti;
    } else {
      new_est_[task_i->index] = kint64min;
    }
  }
 
  // Apply modifications
  bool modified = false;
  for (int i = 0; i < size(); ++i) {
    IntervalVar* const var = by_start_min_[i]->interval;
    if (new_est_[i] != kint64min && (strict_ || var->DurationMin() > 0)) {
      modified = true;
      by_start_min_[i]->interval->SetStartMin(new_est_[i]);
    }
  }
  return modified;
}
 
bool EdgeFinderAndDetectablePrecedences::EdgeFinder() {
  // Initialization.
  UpdateEst();
  for (int i = 0; i < size(); ++i) {
    new_est_[i] = by_start_min_[i]->interval->StartMin();
  }
 
  // Push in one direction.
  std::sort(by_end_max_.begin(), by_end_max_.end(),
            EndMaxLessThan<DisjunctiveTask>);
  lt_tree_.Clear();
  for (int i = 0; i < size(); ++i) {
    lt_tree_.Insert(*by_start_min_[i]);
    DCHECK_EQ(i, by_start_min_[i]->index);
  }
  for (int j = size() - 2; j >= 0; --j) {
    lt_tree_.Grey(*by_end_max_[j + 1]);
    DisjunctiveTask* const twj = by_end_max_[j];
    // We should have checked for overloading earlier.
    DCHECK_LE(lt_tree_.Ect(), twj->interval->EndMax());
    while (lt_tree_.EctOpt() > twj->interval->EndMax()) {
      const int i = lt_tree_.ResponsibleOpt();
      DCHECK_GE(i, 0);
      if (lt_tree_.Ect() > new_est_[i]) {
        new_est_[i] = lt_tree_.Ect();
      }
      lt_tree_.Reset(i);
    }
  }
 
  // Apply modifications.
  bool modified = false;
  for (int i = 0; i < size(); ++i) {
    IntervalVar* const var = by_start_min_[i]->interval;
    if (var->StartMin() < new_est_[i] && (strict_ || var->DurationMin() > 0)) {
      modified = true;
      var->SetStartMin(new_est_[i]);
    }
  }
  return modified;
}
 
// --------- Disjunctive Constraint ----------
 
// ----- Propagation on ranked activities -----
 
class RankedPropagator : public Constraint {
 public:
  RankedPropagator(Solver* const solver, const std::vector<IntVar*>& nexts,
                   const std::vector<IntervalVar*>& intervals,
                   const std::vector<IntVar*>& slacks,
                   DisjunctiveConstraint* const disjunctive)
      : Constraint(solver),
        nexts_(nexts),
        intervals_(intervals),
        slacks_(slacks),
        disjunctive_(disjunctive),
        partial_sequence_(intervals.size()),
        previous_(intervals.size() + 2, 0) {}
 
  ~RankedPropagator() override {}
 
  void Post() override {
    Demon* const delayed =
        solver()->MakeDelayedConstraintInitialPropagateCallback(this);
    for (int i = 0; i < intervals_.size(); ++i) {
      nexts_[i]->WhenBound(delayed);
      intervals_[i]->WhenAnything(delayed);
      slacks_[i]->WhenRange(delayed);
    }
    nexts_.back()->WhenBound(delayed);
  }
 
  void InitialPropagate() override {
    PropagateNexts();
    PropagateSequence();
  }
 
  void PropagateNexts() {
    Solver* const s = solver();
    const int ranked_first = partial_sequence_.NumFirstRanked();
    const int ranked_last = partial_sequence_.NumLastRanked();
    const int sentinel =
        ranked_last == 0
            ? nexts_.size()
            : partial_sequence_[intervals_.size() - ranked_last] + 1;
    int first = 0;
    int counter = 0;
    while (nexts_[first]->Bound()) {
      DCHECK_NE(first, nexts_[first]->Min());
      first = nexts_[first]->Min();
      if (first == sentinel) {
        return;
      }
      if (++counter > ranked_first) {
        DCHECK(intervals_[first - 1]->MayBePerformed());
        partial_sequence_.RankFirst(s, first - 1);
        VLOG(2) << "RankFirst " << first - 1 << " -> "
                << partial_sequence_.DebugString();
      }
    }
    previous_.assign(previous_.size(), -1);
    for (int i = 0; i < nexts_.size(); ++i) {
      if (nexts_[i]->Bound()) {
        previous_[nexts_[i]->Min()] = i;
      }
    }
    int last = previous_.size() - 1;
    counter = 0;
    while (previous_[last] != -1) {
      last = previous_[last];
      if (++counter > ranked_last) {
        partial_sequence_.RankLast(s, last - 1);
        VLOG(2) << "RankLast " << last - 1 << " -> "
                << partial_sequence_.DebugString();
      }
    }
  }
 
  void PropagateSequence() {
    const int last_position = intervals_.size() - 1;
    const int first_sentinel = partial_sequence_.NumFirstRanked();
    const int last_sentinel = last_position - partial_sequence_.NumLastRanked();
    // Propagates on ranked first from left to right.
    for (int i = 0; i < first_sentinel - 1; ++i) {
      IntervalVar* const interval = RankedInterval(i);
      IntervalVar* const next_interval = RankedInterval(i + 1);
      IntVar* const slack = RankedSlack(i);
      const int64 transition_time = RankedTransitionTime(i, i + 1);
      next_interval->SetStartRange(
          CapAdd(interval->StartMin(), CapAdd(slack->Min(), transition_time)),
          CapAdd(interval->StartMax(), CapAdd(slack->Max(), transition_time)));
    }
    // Propagates on ranked last from right to left.
    for (int i = last_position; i > last_sentinel + 1; --i) {
      IntervalVar* const interval = RankedInterval(i - 1);
      IntervalVar* const next_interval = RankedInterval(i);
      IntVar* const slack = RankedSlack(i - 1);
      const int64 transition_time = RankedTransitionTime(i - 1, i);
      interval->SetStartRange(CapSub(next_interval->StartMin(),
                                     CapAdd(slack->Max(), transition_time)),
                              CapSub(next_interval->StartMax(),
                                     CapAdd(slack->Min(), transition_time)));
    }
    // Propagate across.
    IntervalVar* const first_interval =
        first_sentinel > 0 ? RankedInterval(first_sentinel - 1) : nullptr;
    IntVar* const first_slack =
        first_sentinel > 0 ? RankedSlack(first_sentinel - 1) : nullptr;
    IntervalVar* const last_interval = last_sentinel < last_position
                                           ? RankedInterval(last_sentinel + 1)
                                           : nullptr;
 
    // Nothing to do afterwards, exiting.
    if (first_interval == nullptr && last_interval == nullptr) {
      return;
    }
    // Propagates to the middle part.
    // This assumes triangular inequality in the transition times.
    for (int i = first_sentinel; i <= last_sentinel; ++i) {
      IntervalVar* const interval = RankedInterval(i);
      IntVar* const slack = RankedSlack(i);
      if (interval->MayBePerformed()) {
        const bool performed = interval->MustBePerformed();
        if (first_interval != nullptr) {
          const int64 transition_time =
              RankedTransitionTime(first_sentinel - 1, i);
          interval->SetStartRange(
              CapAdd(first_interval->StartMin(),
                     CapAdd(first_slack->Min(), transition_time)),
              CapAdd(first_interval->StartMax(),
                     CapAdd(first_slack->Max(), transition_time)));
          if (performed) {
            first_interval->SetStartRange(
                CapSub(interval->StartMin(),
                       CapAdd(first_slack->Max(), transition_time)),
                CapSub(interval->StartMax(),
                       CapAdd(first_slack->Min(), transition_time)));
          }
        }
        if (last_interval != nullptr) {
          const int64 transition_time =
              RankedTransitionTime(i, last_sentinel + 1);
          interval->SetStartRange(
              CapSub(last_interval->StartMin(),
                     CapAdd(slack->Max(), transition_time)),
              CapSub(last_interval->StartMax(),
                     CapAdd(slack->Min(), transition_time)));
          if (performed) {
            last_interval->SetStartRange(
                CapAdd(interval->StartMin(),
                       CapAdd(slack->Min(), transition_time)),
                CapAdd(interval->StartMax(),
                       CapAdd(slack->Max(), transition_time)));
          }
        }
      }
    }
    // TODO(user): cache transition on ranked intervals in a vector.
    // Propagates on ranked first from right to left.
    for (int i = std::min(first_sentinel - 2, last_position - 1); i >= 0; --i) {
      IntervalVar* const interval = RankedInterval(i);
      IntervalVar* const next_interval = RankedInterval(i + 1);
      IntVar* const slack = RankedSlack(i);
      const int64 transition_time = RankedTransitionTime(i, i + 1);
      interval->SetStartRange(CapSub(next_interval->StartMin(),
                                     CapAdd(slack->Max(), transition_time)),
                              CapSub(next_interval->StartMax(),
                                     CapAdd(slack->Min(), transition_time)));
    }
    // Propagates on ranked last from left to right.
    for (int i = last_sentinel + 1; i < last_position - 1; ++i) {
      IntervalVar* const interval = RankedInterval(i);
      IntervalVar* const next_interval = RankedInterval(i + 1);
      IntVar* const slack = RankedSlack(i);
      const int64 transition_time = RankedTransitionTime(i, i + 1);
      next_interval->SetStartRange(
          CapAdd(interval->StartMin(), CapAdd(slack->Min(), transition_time)),
          CapAdd(interval->StartMax(), CapAdd(slack->Max(), transition_time)));
    }
    // TODO(user) : Propagate on slacks.
  }
 
  IntervalVar* RankedInterval(int i) const {
    const int index = partial_sequence_[i];
    return intervals_[index];
  }
 
  IntVar* RankedSlack(int i) const {
    const int index = partial_sequence_[i];
    return slacks_[index];
  }
 
  int64 RankedTransitionTime(int before, int after) const {
    const int before_index = partial_sequence_[before];
    const int after_index = partial_sequence_[after];
 
    return disjunctive_->TransitionTime(before_index, after_index);
  }
 
  std::string DebugString() const override {
    return absl::StrFormat(
        "RankedPropagator([%s], nexts = [%s], intervals = [%s])",
        partial_sequence_.DebugString(), JoinDebugStringPtr(nexts_, ", "),
        JoinDebugStringPtr(intervals_, ", "));
  }
 
  void Accept(ModelVisitor* const visitor) const override {
    LOG(FATAL) << "Not yet implemented";
    // TODO(user): IMPLEMENT ME.
  }
 
 private:
  std::vector<IntVar*> nexts_;
  std::vector<IntervalVar*> intervals_;
  std::vector<IntVar*> slacks_;
  DisjunctiveConstraint* const disjunctive_;
  RevPartialSequence partial_sequence_;
  std::vector<int> previous_;
};
 
// A class that stores several propagators for the sequence constraint, and
// calls them until a fixpoint is reached.
 
class FullDisjunctiveConstraint : public DisjunctiveConstraint {
 public:
  FullDisjunctiveConstraint(Solver* const s,
                            const std::vector<IntervalVar*>& intervals,
                            const std::string& name, bool strict)
      : DisjunctiveConstraint(s, intervals, name),
        sequence_var_(nullptr),
        straight_(s, intervals, false, strict),
        mirror_(s, intervals, true, strict),
        straight_not_last_(s, intervals, false, strict),
        mirror_not_last_(s, intervals, true, strict),
        strict_(strict) {}
 
  ~FullDisjunctiveConstraint() override {}
 
  void Post() override {
    Demon* const d = MakeDelayedConstraintDemon0(
        solver(), this, &FullDisjunctiveConstraint::InitialPropagate,
        "InitialPropagate");
    for (int32 i = 0; i < straight_.size(); ++i) {
      straight_.interval(i)->WhenAnything(d);
    }
  }
 
  void InitialPropagate() override {
    bool all_optional_or_unperformed = true;
    for (const IntervalVar* const interval : intervals_) {
      if (interval->MustBePerformed()) {
        all_optional_or_unperformed = false;
        break;
      }
    }
    if (all_optional_or_unperformed) {  // Nothing to deduce
      return;
    }
 
    bool all_times_fixed = true;
    for (const IntervalVar* const interval : intervals_) {
      if (interval->MayBePerformed() &&
          (interval->StartMin() != interval->StartMax() ||
           interval->DurationMin() != interval->DurationMax() ||
           interval->EndMin() != interval->EndMax())) {
        all_times_fixed = false;
        break;
      }
    }
 
    if (all_times_fixed) {
      PropagatePerformed();
    } else {
      do {
        do {
          do {
            // OverloadChecking is symmetrical. It has the same effect on the
            // straight and the mirrored version.
            straight_.OverloadChecking();
          } while (straight_.DetectablePrecedences() ||
                   mirror_.DetectablePrecedences());
        } while (straight_not_last_.Propagate() ||
                 mirror_not_last_.Propagate());
      } while (straight_.EdgeFinder() || mirror_.EdgeFinder());
    }
  }
 
  bool Intersect(IntervalVar* const i1, IntervalVar* const i2) const {
    return i1->StartMin() < i2->EndMax() && i2->StartMin() < i1->EndMax();
  }
 
  void PropagatePerformed() {
    performed_.clear();
    optional_.clear();
    for (IntervalVar* const interval : intervals_) {
      if (interval->MustBePerformed()) {
        performed_.push_back(interval);
      } else if (interval->MayBePerformed()) {
        optional_.push_back(interval);
      }
    }
    // Checks feasibility of performed;
    if (performed_.empty()) return;
    std::sort(performed_.begin(), performed_.end(), IntervalStartMinLessThan);
    for (int i = 0; i < performed_.size() - 1; ++i) {
      if (performed_[i]->EndMax() > performed_[i + 1]->StartMin()) {
        solver()->Fail();
      }
    }
 
    // Checks if optional intervals can be inserted.
    if (optional_.empty()) return;
    int index = 0;
    const int num_performed = performed_.size();
    std::sort(optional_.begin(), optional_.end(), IntervalStartMinLessThan);
    for (IntervalVar* const candidate : optional_) {
      const int64 start = candidate->StartMin();
      while (index < num_performed && start >= performed_[index]->EndMax()) {
        index++;
      }
      if (index == num_performed) return;
      if (Intersect(candidate, performed_[index]) ||
          (index < num_performed - 1 &&
           Intersect(candidate, performed_[index + 1]))) {
        candidate->SetPerformed(false);
      }
    }
  }
 
  void Accept(ModelVisitor* const visitor) const override {
    visitor->BeginVisitConstraint(ModelVisitor::kDisjunctive, this);
    visitor->VisitIntervalArrayArgument(ModelVisitor::kIntervalsArgument,
                                        intervals_);
    if (sequence_var_ != nullptr) {
      visitor->VisitSequenceArgument(ModelVisitor::kSequenceArgument,
                                     sequence_var_);
    }
    visitor->EndVisitConstraint(ModelVisitor::kDisjunctive, this);
  }
 
  SequenceVar* MakeSequenceVar() override {
    BuildNextModelIfNeeded();
    if (sequence_var_ == nullptr) {
      solver()->SaveValue(reinterpret_cast<void**>(&sequence_var_));
      sequence_var_ = solver()->RevAlloc(
          new SequenceVar(solver(), intervals_, nexts_, name()));
    }
    return sequence_var_;
  }
 
  std::string DebugString() const override {
    return absl::StrFormat("FullDisjunctiveConstraint([%s], %i)",
                           JoinDebugStringPtr(intervals_, ", "), strict_);
  }
 
  const std::vector<IntVar*>& nexts() const override { return nexts_; }
 
  const std::vector<IntVar*>& actives() const override { return actives_; }
 
  const std::vector<IntVar*>& time_cumuls() const override {
    return time_cumuls_;
  }
 
  const std::vector<IntVar*>& time_slacks() const override {
    return time_slacks_;
  }
 
 private:
  int64 Distance(int64 activity_plus_one, int64 next_activity_plus_one) {
    return (activity_plus_one == 0 ||
            next_activity_plus_one > intervals_.size())
               ? 0
               : transition_time_(activity_plus_one - 1,
                                  next_activity_plus_one - 1);
  }
 
  void BuildNextModelIfNeeded() {
    if (!nexts_.empty()) {
      return;
    }
    Solver* const s = solver();
    const std::string& ct_name = name();
    const int num_intervals = intervals_.size();
    const int num_nodes = intervals_.size() + 1;
    int64 horizon = 0;
    for (int i = 0; i < intervals_.size(); ++i) {
      if (intervals_[i]->MayBePerformed()) {
        horizon = std::max(horizon, intervals_[i]->EndMax());
      }
    }
 
    // Create the next model.
    s->MakeIntVarArray(num_nodes, 1, num_nodes, ct_name + "_nexts", &nexts_);
    // Alldifferent on the nexts variable (the equivalent problem is a tsp).
    s->AddConstraint(s->MakeAllDifferent(nexts_));
 
    actives_.resize(num_nodes);
    for (int i = 0; i < num_intervals; ++i) {
      actives_[i + 1] = intervals_[i]->PerformedExpr()->Var();
      s->AddConstraint(
          s->MakeIsDifferentCstCt(nexts_[i + 1], i + 1, actives_[i + 1]));
    }
    std::vector<IntVar*> short_actives(actives_.begin() + 1, actives_.end());
    actives_[0] = s->MakeMax(short_actives)->Var();
 
    // No Cycle on the corresponding tsp.
    s->AddConstraint(s->MakeNoCycle(nexts_, actives_));
 
    // Cumul on time.
    time_cumuls_.resize(num_nodes + 1);
    // Slacks between activities.
    time_slacks_.resize(num_nodes);
 
    time_slacks_[0] = s->MakeIntVar(0, horizon, "initial_slack");
    // TODO(user): check this.
    time_cumuls_[0] = s->MakeIntConst(0);
 
    for (int64 i = 0; i < num_intervals; ++i) {
      IntervalVar* const var = intervals_[i];
      if (var->MayBePerformed()) {
        const int64 duration_min = var->DurationMin();
        time_slacks_[i + 1] = s->MakeIntVar(
            duration_min, horizon, absl::StrFormat("time_slacks(%d)", i + 1));
        // TODO(user): Check SafeStartExpr();
        time_cumuls_[i + 1] = var->SafeStartExpr(var->StartMin())->Var();
        if (var->DurationMax() != duration_min) {
          s->AddConstraint(s->MakeGreaterOrEqual(
              time_slacks_[i + 1], var->SafeDurationExpr(duration_min)));
        }
      } else {
        time_slacks_[i + 1] = s->MakeIntVar(
            0, horizon, absl::StrFormat("time_slacks(%d)", i + 1));
        time_cumuls_[i + 1] = s->MakeIntConst(horizon);
      }
    }
    // TODO(user): Find a better UB for the last time cumul.
    time_cumuls_[num_nodes] = s->MakeIntVar(0, 2 * horizon, ct_name + "_ect");
    s->AddConstraint(
        s->MakePathCumul(nexts_, actives_, time_cumuls_, time_slacks_,
                         [this](int64 x, int64 y) { return Distance(x, y); }));
 
    std::vector<IntVar*> short_slacks(time_slacks_.begin() + 1,
                                      time_slacks_.end());
    s->AddConstraint(s->RevAlloc(
        new RankedPropagator(s, nexts_, intervals_, short_slacks, this)));
  }
 
  SequenceVar* sequence_var_;
  EdgeFinderAndDetectablePrecedences straight_;
  EdgeFinderAndDetectablePrecedences mirror_;
  NotLast straight_not_last_;
  NotLast mirror_not_last_;
  std::vector<IntVar*> nexts_;
  std::vector<IntVar*> actives_;
  std::vector<IntVar*> time_cumuls_;
  std::vector<IntVar*> time_slacks_;
  std::vector<IntervalVar*> performed_;
  std::vector<IntervalVar*> optional_;
  const bool strict_;
  DISALLOW_COPY_AND_ASSIGN(FullDisjunctiveConstraint);
};
 
// =====================================================================
//  Cumulative
// =====================================================================
 
// A cumulative Theta node, where two energies, corresponding to 2 capacities,
// are stored.
struct DualCapacityThetaNode {
  // Special value for task indices meaning 'no such task'.
  static const int kNone;
 
  // Identity constructor
  DualCapacityThetaNode()
      : energy(0LL),
        energetic_end_min(kint64min),
        residual_energetic_end_min(kint64min) {}
 
  // Constructor for a single cumulative task in the Theta set.
  DualCapacityThetaNode(int64 capacity, int64 residual_capacity,
                        const CumulativeTask& task)
      : energy(task.EnergyMin()),
        energetic_end_min(CapAdd(capacity * task.interval->StartMin(), energy)),
        residual_energetic_end_min(
            CapAdd(residual_capacity * task.interval->StartMin(), energy)) {}
 
  // Constructor for a single variable cumulative task in the Theta set.
  DualCapacityThetaNode(int64 capacity, int64 residual_capacity,
                        const VariableCumulativeTask& task)
      : energy(task.EnergyMin()),
        energetic_end_min(CapAdd(capacity * task.interval->StartMin(), energy)),
        residual_energetic_end_min(
            CapAdd(residual_capacity * task.interval->StartMin(), energy)) {}
 
  // Sets this DualCapacityThetaNode to the result of the natural binary
  // operation over the two given operands, corresponding to the following set
  // operation: Theta = left.Theta union right.Theta
  //
  // No set operation actually occur: we only maintain the relevant quantities
  // associated with such sets.
  void Compute(const DualCapacityThetaNode& left,
               const DualCapacityThetaNode& right) {
    energy = CapAdd(left.energy, right.energy);
    energetic_end_min = std::max(CapAdd(left.energetic_end_min, right.energy),
                                 right.energetic_end_min);
    residual_energetic_end_min =
        std::max(CapAdd(left.residual_energetic_end_min, right.energy),
                 right.residual_energetic_end_min);
  }
 
  // Amount of resource consumed by the Theta set, in units of demand X time.
  // This is energy(Theta).
  int64 energy;
 
  // Max_{subset S of Theta} (capacity * start_min(S) + energy(S))
  int64 energetic_end_min;
 
  // Max_{subset S of Theta} (residual_capacity * start_min(S) + energy(S))
  int64 residual_energetic_end_min;
};
 
const int DualCapacityThetaNode::kNone = -1;
 
// A tree for dual capacity theta nodes
class DualCapacityThetaTree
    : public MonoidOperationTree<DualCapacityThetaNode> {
 public:
  static const int64 kNotInitialized;
 
  explicit DualCapacityThetaTree(int size)
      : MonoidOperationTree<DualCapacityThetaNode>(size),
        capacity_max_(-1),
        residual_capacity_(-1) {}
 
  virtual ~DualCapacityThetaTree() {}
 
  void Init(int64 capacity_max, int64 residual_capacity) {
    DCHECK_LE(0, residual_capacity);
    DCHECK_LE(residual_capacity, capacity_max);
    Clear();
    capacity_max_ = capacity_max;
    residual_capacity_ = residual_capacity;
  }
 
  void Insert(const CumulativeTask* task) {
    Set(task->index,
        DualCapacityThetaNode(capacity_max_, residual_capacity_, *task));
  }
 
  void Insert(const VariableCumulativeTask* task) {
    Set(task->index,
        DualCapacityThetaNode(capacity_max_, residual_capacity_, *task));
  }
 
 private:
  int64 capacity_max_;
  int64 residual_capacity_;
  DISALLOW_COPY_AND_ASSIGN(DualCapacityThetaTree);
};
 
const int64 DualCapacityThetaTree::kNotInitialized = -1LL;
 
// An object that can dive down a branch of a DualCapacityThetaTree to compute
// Env(j, c) in Petr Vilim's notations.
//
// In 'Edge finding filtering algorithm for discrete cumulative resources in
// O(kn log n)' by Petr Vilim, this corresponds to line 6--8 in algorithm 1.3,
// plus all of algorithm 1.2.
//
// http://vilim.eu/petr/cp2009.pdf
// Note: use the version pointed to by this pointer, not the version from the
// conference proceedings, which has a few errors.
class EnvJCComputeDiver {
 public:
  static const int64 kNotAvailable;
  explicit EnvJCComputeDiver(int energy_threshold)
      : energy_threshold_(energy_threshold),
        energy_alpha_(kNotAvailable),
        energetic_end_min_alpha_(kNotAvailable) {}
  void OnArgumentReached(int index, const DualCapacityThetaNode& argument) {
    energy_alpha_ = argument.energy;
    energetic_end_min_alpha_ = argument.energetic_end_min;
    // We should reach a leaf that is not the identity
    // DCHECK_GT(energetic_end_min_alpha_, kint64min);
    // TODO(user): Check me.
  }
  bool ChooseGoLeft(const DualCapacityThetaNode& current,
                    const DualCapacityThetaNode& left_child,
                    const DualCapacityThetaNode& right_child) {
    if (right_child.residual_energetic_end_min > energy_threshold_) {
      return false;  // enough energy on right
    } else {
      energy_threshold_ -= right_child.energy;
      return true;
    }
  }
  void OnComeBackFromLeft(const DualCapacityThetaNode& current,
                          const DualCapacityThetaNode& left_child,
                          const DualCapacityThetaNode& right_child) {
    // The left subtree intersects the alpha set.
    // The right subtree does not intersect the alpha set.
    // The energy_alpha_ and energetic_end_min_alpha_ previously
    // computed are valid for this node too: there's nothing to do.
  }
  void OnComeBackFromRight(const DualCapacityThetaNode& current,
                           const DualCapacityThetaNode& left_child,
                           const DualCapacityThetaNode& right_child) {
    // The left subtree is included in the alpha set.
    // The right subtree intersects the alpha set.
    energetic_end_min_alpha_ =
        std::max(energetic_end_min_alpha_,
                 CapAdd(left_child.energetic_end_min, energy_alpha_));
    energy_alpha_ += left_child.energy;
  }
  int64 GetEnvJC(const DualCapacityThetaNode& root) const {
    const int64 energy = root.energy;
    const int64 energy_beta = CapSub(energy, energy_alpha_);
    return CapAdd(energetic_end_min_alpha_, energy_beta);
  }
 
 private:
  // Energy threshold such that if a set has an energetic_end_min greater than
  // the threshold, then it can push tasks that must end at or after the
  // currently considered end max.
  //
  // Used when diving down only.
  int64 energy_threshold_;
 
  // Energy of the alpha set, that is, the set of tasks whose start min does not
  // exceed the max start min of a set with excess residual energy.
  //
  // Used when swimming up only.
  int64 energy_alpha_;
 
  // Energetic end min of the alpha set.
  //
  // Used when swimming up only.
  int64 energetic_end_min_alpha_;
};
 
const int64 EnvJCComputeDiver::kNotAvailable = -1LL;
 
// In all the following, the term 'update' means 'a potential new start min for
// a task'. The edge-finding algorithm is in two phase: one compute potential
// new start mins, the other detects whether they are applicable or not for each
// task.
 
// Collection of all updates (i.e., potential new start mins) for a given value
// of the demand.
class UpdatesForADemand {
 public:
  explicit UpdatesForADemand(int size)
      : updates_(size, 0), up_to_date_(false) {}
 
  const int64 Update(int index) { return updates_[index]; }
  void Reset() { up_to_date_ = false; }
  void SetUpdate(int index, int64 update) {
    DCHECK(!up_to_date_);
    DCHECK_LT(index, updates_.size());
    updates_[index] = update;
  }
  bool up_to_date() const { return up_to_date_; }
  void set_up_to_date() { up_to_date_ = true; }
 
 private:
  std::vector<int64> updates_;
  bool up_to_date_;
  DISALLOW_COPY_AND_ASSIGN(UpdatesForADemand);
};
 
// One-sided cumulative edge finder.
template <class Task>
class EdgeFinder : public Constraint {
 public:
  EdgeFinder(Solver* const solver, const std::vector<Task*>& tasks,
             IntVar* const capacity)
      : Constraint(solver),
        capacity_(capacity),
        tasks_(tasks),
        by_start_min_(tasks.size()),
        by_end_max_(tasks.size()),
        by_end_min_(tasks.size()),
        lt_tree_(tasks.size(), capacity_->Max()),
        dual_capacity_tree_(tasks.size()),
        has_zero_demand_tasks_(true) {}
 
  ~EdgeFinder() override {
    gtl::STLDeleteElements(&tasks_);
    gtl::STLDeleteValues(&update_map_);
  }
 
  void Post() override {
    // Add the demons
    Demon* const demon = MakeDelayedConstraintDemon0(
        solver(), this, &EdgeFinder::InitialPropagate, "RangeChanged");
    for (Task* const task : tasks_) {
      // Delay propagation, as this constraint is not incremental: we pay
      // O(n log n) each time the constraint is awakened.
      task->WhenAnything(demon);
    }
    capacity_->WhenRange(demon);
  }
 
  // The propagation algorithms: checks for overloading, computes new start mins
  // according to the edge-finding rules, and applies them.
  void InitialPropagate() override {
    InitPropagation();
    PropagateBasedOnEndMinGreaterThanEndMax();
    FillInTree();
    PropagateBasedOnEnergy();
    ApplyNewBounds();
  }
 
  void Accept(ModelVisitor* const visitor) const override {
    LOG(FATAL) << "Should Not Be Visited";
  }
 
  std::string DebugString() const override { return "EdgeFinder"; }
 
 private:
  UpdatesForADemand* GetOrMakeUpdate(int64 demand_min) {
    UpdatesForADemand* update = gtl::FindPtrOrNull(update_map_, demand_min);
    if (update == nullptr) {
      update = new UpdatesForADemand(tasks_.size());
      update_map_[demand_min] = update;
    }
    return update;
  }
 
  // Sets the fields in a proper state to run the propagation algorithm.
  void InitPropagation() {
    // Clear the update stack
    start_min_update_.clear();
    // Re_init vectors if has_zero_demand_tasks_ is true
    if (has_zero_demand_tasks_.Value()) {
      by_start_min_.clear();
      by_end_min_.clear();
      by_end_max_.clear();
      // Only populate tasks with demand_min > 0.
      bool zero_demand = false;
      for (Task* const task : tasks_) {
        if (task->DemandMin() > 0) {
          by_start_min_.push_back(task);
          by_end_min_.push_back(task);
          by_end_max_.push_back(task);
        } else {
          zero_demand = true;
        }
      }
      if (!zero_demand) {
        has_zero_demand_tasks_.SetValue(solver(), false);
      }
    }
 
    // sort by start min.
    std::sort(by_start_min_.begin(), by_start_min_.end(),
              StartMinLessThan<Task>);
    for (int i = 0; i < by_start_min_.size(); ++i) {
      by_start_min_[i]->index = i;
    }
    // Sort by end max.
    std::sort(by_end_max_.begin(), by_end_max_.end(), EndMaxLessThan<Task>);
    // Sort by end min.
    std::sort(by_end_min_.begin(), by_end_min_.end(), EndMinLessThan<Task>);
    // Initialize the tree with the new capacity.
    lt_tree_.Init(capacity_->Max());
    // Clear updates
    for (const auto& entry : update_map_) {
      entry.second->Reset();
    }
  }
 
  // Computes all possible update values for tasks of given demand, and stores
  // these values in update_map_[demand].
  // Runs in O(n log n).
  // This corresponds to lines 2--13 in algorithm 1.3 in Petr Vilim's paper.
  void ComputeConditionalStartMins(UpdatesForADemand* updates,
                                   int64 demand_min) {
    DCHECK_GT(demand_min, 0);
    DCHECK(updates != nullptr);
    const int64 capacity_max = capacity_->Max();
    const int64 residual_capacity = CapSub(capacity_max, demand_min);
    dual_capacity_tree_.Init(capacity_max, residual_capacity);
    // It's important to initialize the update at IntervalVar::kMinValidValue
    // rather than at kInt64min, because its opposite may be used if it's a
    // mirror variable, and
    // -kInt64min = -(-kInt64max - 1) = kInt64max + 1 = -kInt64min
    int64 update = IntervalVar::kMinValidValue;
    for (int i = 0; i < by_end_max_.size(); ++i) {
      Task* const task = by_end_max_[i];
      if (task->EnergyMin() == 0) continue;
      const int64 current_end_max = task->interval->EndMax();
      dual_capacity_tree_.Insert(task);
      const int64 energy_threshold = residual_capacity * current_end_max;
      const DualCapacityThetaNode& root = dual_capacity_tree_.result();
      const int64 res_energetic_end_min = root.residual_energetic_end_min;
      if (res_energetic_end_min > energy_threshold) {
        EnvJCComputeDiver diver(energy_threshold);
        dual_capacity_tree_.DiveInTree(&diver);
        const int64 enjv = diver.GetEnvJC(dual_capacity_tree_.result());
        const int64 numerator = CapSub(enjv, energy_threshold);
        const int64 diff = MathUtil::CeilOfRatio(numerator, demand_min);
        update = std::max(update, diff);
      }
      updates->SetUpdate(i, update);
    }
    updates->set_up_to_date();
  }
 
  // Returns the new start min that can be inferred for task_to_push if it is
  // proved that it cannot end before by_end_max[end_max_index] does.
  int64 ConditionalStartMin(const Task& task_to_push, int end_max_index) {
    if (task_to_push.EnergyMin() == 0) {
      return task_to_push.interval->StartMin();
    }
    const int64 demand_min = task_to_push.DemandMin();
    UpdatesForADemand* const updates = GetOrMakeUpdate(demand_min);
    if (!updates->up_to_date()) {
      ComputeConditionalStartMins(updates, demand_min);
    }
    DCHECK(updates->up_to_date());
    return updates->Update(end_max_index);
  }
 
  // Propagates by discovering all end-after-end relationships purely based on
  // comparisons between end mins and end maxes: there is no energetic reasoning
  // here, but this allow updates that the standard edge-finding detection rule
  // misses.
  // See paragraph 6.2 in http://vilim.eu/petr/cp2009.pdf.
  void PropagateBasedOnEndMinGreaterThanEndMax() {
    int end_max_index = 0;
    int64 max_start_min = kint64min;
    for (Task* const task : by_end_min_) {
      const int64 end_min = task->interval->EndMin();
      while (end_max_index < by_start_min_.size() &&
             by_end_max_[end_max_index]->interval->EndMax() <= end_min) {
        max_start_min = std::max(
            max_start_min, by_end_max_[end_max_index]->interval->StartMin());
        ++end_max_index;
      }
      if (end_max_index > 0 && task->interval->StartMin() <= max_start_min &&
          task->interval->EndMax() > task->interval->EndMin()) {
        DCHECK_LE(by_end_max_[end_max_index - 1]->interval->EndMax(), end_min);
        // The update is valid and may be interesting:
        // * If task->StartMin() > max_start_min, then all tasks whose end_max
        //     is less than or equal to end_min have a start min that is less
        //     than task->StartMin(). In this case, any update we could
        //     compute would also be computed by the standard edge-finding
        //     rule. It's better not to compute it, then: it may not be
        //     needed.
        // * If task->EndMax() <= task->EndMin(), that means the end max is
        //     bound. In that case, 'task' itself belong to the set of tasks
        //     that must end before end_min, which may cause the result of
        //     ConditionalStartMin(task, end_max_index - 1) not to be a valid
        //     update.
        const int64 update = ConditionalStartMin(*task, end_max_index - 1);
        start_min_update_.push_back(std::make_pair(task->interval, update));
      }
    }
  }
 
  // Fill the theta-lambda-tree, and check for overloading.
  void FillInTree() {
    for (Task* const task : by_end_max_) {
      lt_tree_.Insert(*task);
      // Maximum energetic end min without overload.
      const int64 max_feasible =
          CapProd(capacity_->Max(), task->interval->EndMax());
      if (lt_tree_.energetic_end_min() > max_feasible) {
        solver()->Fail();
      }
    }
  }
 
  // The heart of the propagation algorithm. Should be called with all tasks
  // being in the Theta set. It detects tasks that need to be pushed.
  void PropagateBasedOnEnergy() {
    for (int j = by_start_min_.size() - 2; j >= 0; --j) {
      lt_tree_.Grey(*by_end_max_[j + 1]);
      Task* const twj = by_end_max_[j];
      // We should have checked for overload earlier.
      const int64 max_feasible =
          CapProd(capacity_->Max(), twj->interval->EndMax());
      DCHECK_LE(lt_tree_.energetic_end_min(), max_feasible);
      while (lt_tree_.energetic_end_min_opt() > max_feasible) {
        const int i = lt_tree_.argmax_energetic_end_min_opt();
        DCHECK_GE(i, 0);
        PropagateTaskCannotEndBefore(i, j);
        lt_tree_.Reset(i);
      }
    }
  }
 
  // Takes into account the fact that the task of given index cannot end before
  // the given new end min.
  void PropagateTaskCannotEndBefore(int index, int end_max_index) {
    Task* const task_to_push = by_start_min_[index];
    const int64 update = ConditionalStartMin(*task_to_push, end_max_index);
    start_min_update_.push_back(std::make_pair(task_to_push->interval, update));
  }
 
  // Applies the previously computed updates.
  void ApplyNewBounds() {
    for (const std::pair<IntervalVar*, int64>& update : start_min_update_) {
      update.first->SetStartMin(update.second);
    }
  }
 
  // Capacity of the cumulative resource.
  IntVar* const capacity_;
 
  // Initial vector of tasks
  std::vector<Task*> tasks_;
 
  // Cumulative tasks, ordered by non-decreasing start min.
  std::vector<Task*> by_start_min_;
 
  // Cumulative tasks, ordered by non-decreasing end max.
  std::vector<Task*> by_end_max_;
 
  // Cumulative tasks, ordered by non-decreasing end min.
  std::vector<Task*> by_end_min_;
 
  // Cumulative theta-lamba tree.
  CumulativeLambdaThetaTree lt_tree_;
 
  // Needed by ComputeConditionalStartMins.
  DualCapacityThetaTree dual_capacity_tree_;
 
  // Stack of updates to the new start min to do.
  std::vector<std::pair<IntervalVar*, int64>> start_min_update_;
 
  // update_map_[d][i] is an integer such that if a task
  // whose demand is d cannot end before by_end_max_[i], then it cannot start
  // before update_map_[d][i].
  absl::flat_hash_map<int64, UpdatesForADemand*> update_map_;
 
  // Has one task a demand min == 0
  Rev<bool> has_zero_demand_tasks_;
 
  DISALLOW_COPY_AND_ASSIGN(EdgeFinder);
};
 
// A point in time where the usage profile changes.
// Starting from time (included), the usage is what it was immediately before
// time, plus the delta.
//
// Example:
// Consider the following vector of ProfileDelta's:
// { t=1, d=+3}, { t=4, d=+1 }, { t=5, d=-2}, { t=8, d=-1}
// This represents the following usage profile:
//
// usage
// 4 |                   ****.
// 3 |       ************.   .
// 2 |       .           .   ************.
// 1 |       .           .   .           .
// 0 |*******----------------------------*******************-> time
//       0   1   2   3   4   5   6   7   8   9
//
// Note that the usage profile is right-continuous (see
// http://en.wikipedia.org/wiki/Left-continuous#Directional_continuity).
// This is because intervals for tasks are always closed on the start side
// and open on the end side.
struct ProfileDelta {
  ProfileDelta(int64 _time, int64 _delta) : time(_time), delta(_delta) {}
  int64 time;
  int64 delta;
};
 
bool TimeLessThan(const ProfileDelta& delta1, const ProfileDelta& delta2) {
  return delta1.time < delta2.time;
}
 
// Cumulative time-table.
//
// This class implements a propagator for the CumulativeConstraint which is not
// incremental, and where a call to InitialPropagate() takes time which is
// O(n^2) and Omega(n log n) with n the number of cumulative tasks.
//
// Despite the high complexity, this propagator is needed, because of those
// implemented, it is the only one that satisfy that if all instantiated, no
// contradiction will be detected if and only if the constraint is satisfied.
//
// The implementation is quite naive, and could certainly be improved, for
// example by maintaining the profile incrementally.
template <class Task>
class CumulativeTimeTable : public Constraint {
 public:
  CumulativeTimeTable(Solver* const solver, const std::vector<Task*>& tasks,
                      IntVar* const capacity)
      : Constraint(solver), by_start_min_(tasks), capacity_(capacity) {
    // There may be up to 2 delta's per interval (one on each side),
    // plus two sentinels
    const int profile_max_size = 2 * by_start_min_.size() + 2;
    profile_non_unique_time_.reserve(profile_max_size);
    profile_unique_time_.reserve(profile_max_size);
  }
 
  ~CumulativeTimeTable() override { gtl::STLDeleteElements(&by_start_min_); }
 
  void InitialPropagate() override {
    BuildProfile();
    PushTasks();
    // TODO(user): When a task has a fixed part, we could propagate
    // max_demand from its current location.
  }
 
  void Post() override {
    Demon* demon = MakeDelayedConstraintDemon0(
        solver(), this, &CumulativeTimeTable::InitialPropagate,
        "InitialPropagate");
    for (Task* const task : by_start_min_) {
      task->WhenAnything(demon);
    }
    capacity_->WhenRange(demon);
  }
 
  void Accept(ModelVisitor* const visitor) const override {
    LOG(FATAL) << "Should not be visited";
  }
 
  std::string DebugString() const override { return "CumulativeTimeTable"; }
 
 private:
  // Build the usage profile. Runs in O(n log n).
  void BuildProfile() {
    // Build profile with non unique time
    profile_non_unique_time_.clear();
    for (const Task* const task : by_start_min_) {
      const IntervalVar* const interval = task->interval;
      const int64 start_max = interval->StartMax();
      const int64 end_min = interval->EndMin();
      if (interval->MustBePerformed() && start_max < end_min) {
        const int64 demand_min = task->DemandMin();
        if (demand_min > 0) {
          profile_non_unique_time_.emplace_back(start_max, +demand_min);
          profile_non_unique_time_.emplace_back(end_min, -demand_min);
        }
      }
    }
    // Sort
    std::sort(profile_non_unique_time_.begin(), profile_non_unique_time_.end(),
              TimeLessThan);
    // Build profile with unique times
    profile_unique_time_.clear();
    profile_unique_time_.emplace_back(kint64min, 0);
    int64 usage = 0;
    for (const ProfileDelta& step : profile_non_unique_time_) {
      if (step.time == profile_unique_time_.back().time) {
        profile_unique_time_.back().delta += step.delta;
      } else {
        profile_unique_time_.push_back(step);
      }
      // Update usage.
      usage += step.delta;
    }
    // Check final usage to be 0.
    DCHECK_EQ(0, usage);
    // Scan to find max usage.
    int64 max_usage = 0;
    for (const ProfileDelta& step : profile_unique_time_) {
      usage += step.delta;
      if (usage > max_usage) {
        max_usage = usage;
      }
    }
    DCHECK_EQ(0, usage);
    capacity_->SetMin(max_usage);
    // Add a sentinel.
    profile_unique_time_.emplace_back(kint64max, 0);
  }
 
  // Update the start min for all tasks. Runs in O(n^2) and Omega(n).
  void PushTasks() {
    std::sort(by_start_min_.begin(), by_start_min_.end(),
              StartMinLessThan<Task>);
    int64 usage = 0;
    int profile_index = 0;
    for (const Task* const task : by_start_min_) {
      const IntervalVar* const interval = task->interval;
      if (interval->StartMin() == interval->StartMax() &&
          interval->EndMin() == interval->EndMax()) {
        continue;
      }
      while (interval->StartMin() > profile_unique_time_[profile_index].time) {
        DCHECK(profile_index < profile_unique_time_.size());
        ++profile_index;
        usage += profile_unique_time_[profile_index].delta;
      }
      PushTask(task, profile_index, usage);
    }
  }
 
  // Push the given task to new_start_min, defined as the smallest integer such
  // that the profile usage for all tasks, excluding the current one, does not
  // exceed capacity_ - task->demand on the interval
  // [new_start_min, new_start_min + task->interval->DurationMin() ).
  void PushTask(const Task* const task, int profile_index, int64 usage) {
    // Init
    const IntervalVar* const interval = task->interval;
    const int64 demand_min = task->DemandMin();
    if (demand_min == 0) {  // Demand can be null, nothing to propagate.
      return;
    }
    const int64 residual_capacity = CapSub(capacity_->Max(), demand_min);
    const int64 duration = task->interval->DurationMin();
    const ProfileDelta& first_prof_delta = profile_unique_time_[profile_index];
 
    int64 new_start_min = interval->StartMin();
 
    DCHECK_GE(first_prof_delta.time, interval->StartMin());
    // The check above is with a '>='. Let's first treat the '>' case
    if (first_prof_delta.time > interval->StartMin()) {
      // There was no profile delta at a time between interval->StartMin()
      // (included) and the current one.
      // As we don't delete delta's of 0 value, this means the current task
      // does not contribute to the usage before:
      DCHECK((interval->StartMax() >= first_prof_delta.time) ||
             (interval->StartMax() >= interval->EndMin()));
      // The 'usage' given in argument is valid at first_prof_delta.time. To
      // compute the usage at the start min, we need to remove the last delta.
      const int64 usage_at_start_min = CapSub(usage, first_prof_delta.delta);
      if (usage_at_start_min > residual_capacity) {
        new_start_min = profile_unique_time_[profile_index].time;
      }
    }
 
    // Influence of current task
    const int64 start_max = interval->StartMax();
    const int64 end_min = interval->EndMin();
    ProfileDelta delta_start(start_max, 0);
    ProfileDelta delta_end(end_min, 0);
    if (interval->MustBePerformed() && start_max < end_min) {
      delta_start.delta = +demand_min;
      delta_end.delta = -demand_min;
    }
    while (profile_unique_time_[profile_index].time <
           CapAdd(duration, new_start_min)) {
      const ProfileDelta& profile_delta = profile_unique_time_[profile_index];
      DCHECK(profile_index < profile_unique_time_.size());
      // Compensate for current task
      if (profile_delta.time == delta_start.time) {
        usage -= delta_start.delta;
      }
      if (profile_delta.time == delta_end.time) {
        usage -= delta_end.delta;
      }
      // Increment time
      ++profile_index;
      DCHECK(profile_index < profile_unique_time_.size());
      // Does it fit?
      if (usage > residual_capacity) {
        new_start_min = profile_unique_time_[profile_index].time;
      }
      usage += profile_unique_time_[profile_index].delta;
    }
    task->interval->SetStartMin(new_start_min);
  }
 
  typedef std::vector<ProfileDelta> Profile;
 
  Profile profile_unique_time_;
  Profile profile_non_unique_time_;
  std::vector<Task*> by_start_min_;
  IntVar* const capacity_;
 
  DISALLOW_COPY_AND_ASSIGN(CumulativeTimeTable);
};
 
// Cumulative idempotent Time-Table.
//
// This propagator is based on Letort et al. 2012 add Gay et al. 2015.
//
// TODO(user): fill the description once the incremental aspect are
// implemented.
//
// Worst case: O(n^2 log n) -- really unlikely in practice.
// Best case:  Omega(1).
// Practical:  Almost linear in the number of unfixed tasks.
template <class Task>
class TimeTableSync : public Constraint {
 public:
  TimeTableSync(Solver* const solver, const std::vector<Task*>& tasks,
                IntVar* const capacity)
      : Constraint(solver), tasks_(tasks), capacity_(capacity) {
    num_tasks_ = tasks_.size();
    gap_ = 0;
    prev_gap_ = 0;
    pos_ = kint64min;
    next_pos_ = kint64min;
    // Allocate vectors to contain no more than n_tasks.
    start_min_.reserve(num_tasks_);
    start_max_.reserve(num_tasks_);
    end_min_.reserve(num_tasks_);
    durations_.reserve(num_tasks_);
    demands_.reserve(num_tasks_);
  }
 
  ~TimeTableSync() override { gtl::STLDeleteElements(&tasks_); }
 
  void InitialPropagate() override {
    // Reset data structures.
    BuildEvents();
    while (!events_scp_.empty() && !events_ecp_.empty()) {
      // Move the sweep line.
      pos_ = NextEventTime();
      // Update the profile with compulsory part events.
      ProcessEventsScp();
      ProcessEventsEcp();
      // Update minimum capacity (may fail)
      capacity_->SetMin(capacity_->Max() - gap_);
      // Time to the next possible profile increase.
      next_pos_ = NextScpTime();
      // Consider new task to schedule.
      ProcessEventsPr();
      // Filter.
      FilterMin();
    }
  }
 
  void Post() override {
    Demon* demon = MakeDelayedConstraintDemon0(
        solver(), this, &TimeTableSync::InitialPropagate, "InitialPropagate");
    for (Task* const task : tasks_) {
      task->WhenAnything(demon);
    }
    capacity_->WhenRange(demon);
  }
 
  void Accept(ModelVisitor* const visitor) const override {
    LOG(FATAL) << "Should not be visited";
  }
 
  std::string DebugString() const override { return "TimeTableSync"; }
 
 private:
  // Task state.
  enum State { NONE, READY, CHECK, CONFLICT };
 
  inline int64 NextScpTime() {
    return !events_scp_.empty() ? events_scp_.top().first : kint64max;
  }
 
  inline int64 NextEventTime() {
    int64 time = kint64max;
    if (!events_pr_.empty()) {
      time = events_pr_.top().first;
    }
    if (!events_scp_.empty()) {
      int64 t = events_scp_.top().first;
      time = t < time ? t : time;
    }
    if (!events_ecp_.empty()) {
      int64 t = events_ecp_.top().first;
      time = t < time ? t : time;
    }
    return time;
  }
 
  void ProcessEventsScp() {
    while (!events_scp_.empty() && events_scp_.top().first == pos_) {
      const int64 task_id = events_scp_.top().second;
      events_scp_.pop();
      const int64 old_end_min = end_min_[task_id];
      if (states_[task_id] == State::CONFLICT) {
        // Update cached values.
        const int64 new_end_min = pos_ + durations_[task_id];
        start_min_[task_id] = pos_;
        end_min_[task_id] = new_end_min;
        // Filter the domain
        tasks_[task_id]->interval->SetStartMin(pos_);
      }
      // The task is scheduled.
      states_[task_id] = State::READY;
      // Update the profile if the task has a compulsory part.
      if (pos_ < end_min_[task_id]) {
        gap_ -= demands_[task_id];
        if (old_end_min <= pos_) {
          events_ecp_.push(kv(end_min_[task_id], task_id));
        }
      }
    }
  }
 
  void ProcessEventsEcp() {
    while (!events_ecp_.empty() && events_ecp_.top().first == pos_) {
      const int64 task_id = events_ecp_.top().second;
      events_ecp_.pop();
      // Update the event if it is not up to date.
      if (pos_ < end_min_[task_id]) {
        events_ecp_.push(kv(end_min_[task_id], task_id));
      } else {
        gap_ += demands_[task_id];
      }
    }
  }
 
  void ProcessEventsPr() {
    while (!events_pr_.empty() && events_pr_.top().first == pos_) {
      const int64 task_id = events_pr_.top().second;
      events_pr_.pop();
      // The task is in conflict with the current profile.
      if (demands_[task_id] > gap_) {
        states_[task_id] = State::CONFLICT;
        conflict_.push(kv(demands_[task_id], task_id));
        continue;
      }
      // The task is not in conflict for the moment.
      if (next_pos_ < end_min_[task_id]) {
        states_[task_id] = State::CHECK;
        check_.push(kv(demands_[task_id], task_id));
        continue;
      }
      // The task is not in conflict and can be scheduled.
      states_[task_id] = State::READY;
    }
  }
 
  void FilterMin() {
    // The profile exceeds the capacity.
    capacity_->SetMin(capacity_->Max() - gap_);
    // The profile has increased.
    if (gap_ < prev_gap_) {
      // Reconsider the task in check state.
      while (!check_.empty() && demands_[check_.top().second] > gap_) {
        const int64 task_id = check_.top().second;
        check_.pop();
        if (states_[task_id] == State::CHECK && pos_ < end_min_[task_id]) {
          states_[task_id] = State::CONFLICT;
          conflict_.push(kv(demands_[task_id], task_id));
          continue;
        }
        states_[task_id] = State::READY;
      }
      prev_gap_ = gap_;
    }
    // The profile has decreased.
    if (gap_ > prev_gap_) {
      // Reconsider the tasks in conflict.
      while (!conflict_.empty() && demands_[conflict_.top().second] <= gap_) {
        const int64 task_id = conflict_.top().second;
        conflict_.pop();
        if (states_[task_id] != State::CONFLICT) {
          continue;
        }
        const int64 old_end_min = end_min_[task_id];
        // Update the cache.
        start_min_[task_id] = pos_;
        end_min_[task_id] = pos_ + durations_[task_id];
        // Filter the domain.
        tasks_[task_id]->interval->SetStartMin(pos_);  // should not fail.
        // The task still have to be checked.
        if (next_pos_ < end_min_[task_id]) {
          states_[task_id] = State::CHECK;
          check_.push(kv(demands_[task_id], task_id));
        } else {
          states_[task_id] = State::READY;
        }
        // Update possible compulsory part.
        const int64 start_max = start_max_[task_id];
        if (start_max >= old_end_min && start_max < end_min_[task_id]) {
          events_ecp_.push(kv(end_min_[task_id], task_id));
        }
      }
    }
    prev_gap_ = gap_;
  }
 
  void BuildEvents() {
    // Reset the sweep line.
    pos_ = kint64min;
    next_pos_ = kint64min;
    gap_ = capacity_->Max();
    prev_gap_ = capacity_->Max();
    // Reset dynamic states.
    conflict_ = min_heap();
    check_ = max_heap();
    // Reset profile events.
    events_pr_ = min_heap();
    events_scp_ = min_heap();
    events_ecp_ = min_heap();
    // Reset cache.
    start_min_.clear();
    start_max_.clear();
    end_min_.clear();
    durations_.clear();
    demands_.clear();
    states_.clear();
    // Build events.
    for (int i = 0; i < num_tasks_; i++) {
      const int64 s_min = tasks_[i]->interval->StartMin();
      const int64 s_max = tasks_[i]->interval->StartMax();
      const int64 e_min = tasks_[i]->interval->EndMin();
      // Cache the values.
      start_min_.push_back(s_min);
      start_max_.push_back(s_max);
      end_min_.push_back(e_min);
      durations_.push_back(tasks_[i]->interval->DurationMin());
      demands_.push_back(tasks_[i]->DemandMin());
      // Reset task state.
      states_.push_back(State::NONE);
      // Start compulsory part event.
      events_scp_.push(kv(s_max, i));
      // Pruning event only if the start time of the task is not fixed.
      if (s_min != s_max) {
        events_pr_.push(kv(s_min, i));
      }
      // End of compulsory part only if the task has a compulsory part.
      if (s_max < e_min) {
        events_ecp_.push(kv(e_min, i));
      }
    }
  }
 
  int64 num_tasks_;
  std::vector<Task*> tasks_;
  IntVar* const capacity_;
 
  std::vector<int64> start_min_;
  std::vector<int64> start_max_;
  std::vector<int64> end_min_;
  std::vector<int64> end_max_;
  std::vector<int64> durations_;
  std::vector<int64> demands_;
 
  // Pair key value.
  typedef std::pair<int64, int64> kv;
  typedef std::priority_queue<kv, std::vector<kv>, std::greater<kv>> min_heap;
  typedef std::priority_queue<kv, std::vector<kv>, std::less<kv>> max_heap;
 
  // Profile events.
  min_heap events_pr_;
  min_heap events_scp_;
  min_heap events_ecp_;
 
  // Task state.
  std::vector<State> states_;
  min_heap conflict_;
  max_heap check_;
 
  // Sweep line state.
  int64 pos_;
  int64 next_pos_;
  int64 gap_;
  int64 prev_gap_;
};
 
class CumulativeConstraint : public Constraint {
 public:
  CumulativeConstraint(Solver* const s,
                       const std::vector<IntervalVar*>& intervals,
                       const std::vector<int64>& demands,
                       IntVar* const capacity, const std::string& name)
      : Constraint(s),
        capacity_(capacity),
        intervals_(intervals),
        demands_(demands) {
    tasks_.reserve(intervals.size());
    for (int i = 0; i < intervals.size(); ++i) {
      tasks_.push_back(CumulativeTask(intervals[i], demands[i]));
    }
  }
 
  void Post() override {
    // For the cumulative constraint, there are many propagators, and they
    // don't dominate each other. So the strongest propagation is obtained
    // by posting a bunch of different propagators.
    const ConstraintSolverParameters& params = solver()->parameters();
    if (params.use_cumulative_time_table()) {
      if (params.use_cumulative_time_table_sync()) {
        PostOneSidedConstraint(false, false, true);
        PostOneSidedConstraint(true, false, true);
      } else {
        PostOneSidedConstraint(false, false, false);
        PostOneSidedConstraint(true, false, false);
      }
    }
    if (params.use_cumulative_edge_finder()) {
      PostOneSidedConstraint(false, true, false);
      PostOneSidedConstraint(true, true, false);
    }
    if (params.use_sequence_high_demand_tasks()) {
      PostHighDemandSequenceConstraint();
    }
    if (params.use_all_possible_disjunctions()) {
      PostAllDisjunctions();
    }
  }
 
  void InitialPropagate() override {
    // Nothing to do: this constraint delegates all the work to other classes
  }
 
  void Accept(ModelVisitor* const visitor) const override {
    // TODO(user): Build arrays on demand?
    visitor->BeginVisitConstraint(ModelVisitor::kCumulative, this);
    visitor->VisitIntervalArrayArgument(ModelVisitor::kIntervalsArgument,
                                        intervals_);
    visitor->VisitIntegerArrayArgument(ModelVisitor::kDemandsArgument,
                                       demands_);
    visitor->VisitIntegerExpressionArgument(ModelVisitor::kCapacityArgument,
                                            capacity_);
    visitor->EndVisitConstraint(ModelVisitor::kCumulative, this);
  }
 
  std::string DebugString() const override {
    return absl::StrFormat("CumulativeConstraint([%s], %s)",
                           JoinDebugString(tasks_, ", "),
                           capacity_->DebugString());
  }
 
 private:
  // Post temporal disjunctions for tasks that cannot overlap.
  void PostAllDisjunctions() {
    for (int i = 0; i < intervals_.size(); ++i) {
      IntervalVar* const interval_i = intervals_[i];
      if (interval_i->MayBePerformed()) {
        for (int j = i + 1; j < intervals_.size(); ++j) {
          IntervalVar* const interval_j = intervals_[j];
          if (interval_j->MayBePerformed()) {
            if (CapAdd(tasks_[i].demand, tasks_[j].demand) > capacity_->Max()) {
              Constraint* const constraint =
                  solver()->MakeTemporalDisjunction(interval_i, interval_j);
              solver()->AddConstraint(constraint);
            }
          }
        }
      }
    }
  }
 
  // Post a Sequence constraint for tasks that requires strictly more than half
  // of the resource
  void PostHighDemandSequenceConstraint() {
    Constraint* constraint = nullptr;
    {  // Need a block to avoid memory leaks in case the AddConstraint fails
      std::vector<IntervalVar*> high_demand_intervals;
      high_demand_intervals.reserve(intervals_.size());
      for (int i = 0; i < demands_.size(); ++i) {
        const int64 demand = tasks_[i].demand;
        // Consider two tasks with demand d1 and d2 such that
        // d1 * 2 > capacity_ and d2 * 2 > capacity_.
        // Then d1 + d2 = 1/2 (d1 * 2 + d2 * 2)
        //              > 1/2 (capacity_ + capacity_)
        //              > capacity_.
        // Therefore these two tasks cannot overlap.
        if (demand * 2 > capacity_->Max() &&
            tasks_[i].interval->MayBePerformed()) {
          high_demand_intervals.push_back(tasks_[i].interval);
        }
      }
      if (high_demand_intervals.size() >= 2) {
        // If there are less than 2 such intervals, the constraint would do
        // nothing
        std::string seq_name = absl::StrCat(name(), "-HighDemandSequence");
        constraint = solver()->MakeDisjunctiveConstraint(high_demand_intervals,
                                                         seq_name);
      }
    }
    if (constraint != nullptr) {
      solver()->AddConstraint(constraint);
    }
  }
 
  // Populate the given vector with useful tasks, meaning the ones on which
  // some propagation can be done
  void PopulateVectorUsefulTasks(
      bool mirror, std::vector<CumulativeTask*>* const useful_tasks) {
    DCHECK(useful_tasks->empty());
    for (int i = 0; i < tasks_.size(); ++i) {
      const CumulativeTask& original_task = tasks_[i];
      IntervalVar* const interval = original_task.interval;
      // Check if exceed capacity
      if (original_task.demand > capacity_->Max()) {
        interval->SetPerformed(false);
      }
      // Add to the useful_task vector if it may be performed and that it
      // actually consumes some of the resource.
      if (interval->MayBePerformed() && original_task.demand > 0) {
        Solver* const s = solver();
        IntervalVar* const original_interval = original_task.interval;
        IntervalVar* const interval =
            mirror ? s->MakeMirrorInterval(original_interval)
                   : original_interval;
        IntervalVar* const relaxed_max = s->MakeIntervalRelaxedMax(interval);
        useful_tasks->push_back(
            new CumulativeTask(relaxed_max, original_task.demand));
      }
    }
  }
 
  // Makes and return an edge-finder or a time table, or nullptr if it is not
  // necessary.
  Constraint* MakeOneSidedConstraint(bool mirror, bool edge_finder,
                                     bool tt_sync) {
    std::vector<CumulativeTask*> useful_tasks;
    PopulateVectorUsefulTasks(mirror, &useful_tasks);
    if (useful_tasks.empty()) {
      return nullptr;
    } else {
      Solver* const s = solver();
      if (edge_finder) {
        const ConstraintSolverParameters& params = solver()->parameters();
        return useful_tasks.size() < params.max_edge_finder_size()
                   ? s->RevAlloc(new EdgeFinder<CumulativeTask>(s, useful_tasks,
                                                                capacity_))
                   : nullptr;
      }
      if (tt_sync) {
        return s->RevAlloc(
            new TimeTableSync<CumulativeTask>(s, useful_tasks, capacity_));
      }
      return s->RevAlloc(
          new CumulativeTimeTable<CumulativeTask>(s, useful_tasks, capacity_));
    }
  }
 
  // Post a straight or mirrored edge-finder, if needed
  void PostOneSidedConstraint(bool mirror, bool edge_finder, bool tt_sync) {
    Constraint* const constraint =
        MakeOneSidedConstraint(mirror, edge_finder, tt_sync);
    if (constraint != nullptr) {
      solver()->AddConstraint(constraint);
    }
  }
 
  // Capacity of the cumulative resource
  IntVar* const capacity_;
 
  // The tasks that share the cumulative resource
  std::vector<CumulativeTask> tasks_;
 
  // Array of intervals for the visitor.
  const std::vector<IntervalVar*> intervals_;
  // Array of demands for the visitor.
  const std::vector<int64> demands_;
 
  DISALLOW_COPY_AND_ASSIGN(CumulativeConstraint);
};
 
class VariableDemandCumulativeConstraint : public Constraint {
 public:
  VariableDemandCumulativeConstraint(Solver* const s,
                                     const std::vector<IntervalVar*>& intervals,
                                     const std::vector<IntVar*>& demands,
                                     IntVar* const capacity,
                                     const std::string& name)
      : Constraint(s),
        capacity_(capacity),
        intervals_(intervals),
        demands_(demands) {
    tasks_.reserve(intervals.size());
    for (int i = 0; i < intervals.size(); ++i) {
      tasks_.push_back(VariableCumulativeTask(intervals[i], demands[i]));
    }
  }
 
  void Post() override {
    // For the cumulative constraint, there are many propagators, and they
    // don't dominate each other. So the strongest propagation is obtained
    // by posting a bunch of different propagators.
    const ConstraintSolverParameters& params = solver()->parameters();
    if (params.use_cumulative_time_table()) {
      PostOneSidedConstraint(false, false, false);
      PostOneSidedConstraint(true, false, false);
    }
    if (params.use_cumulative_edge_finder()) {
      PostOneSidedConstraint(false, true, false);
      PostOneSidedConstraint(true, true, false);
    }
    if (params.use_sequence_high_demand_tasks()) {
      PostHighDemandSequenceConstraint();
    }
    if (params.use_all_possible_disjunctions()) {
      PostAllDisjunctions();
    }
  }
 
  void InitialPropagate() override {
    // Nothing to do: this constraint delegates all the work to other classes
  }
 
  void Accept(ModelVisitor* const visitor) const override {
    // TODO(user): Build arrays on demand?
    visitor->BeginVisitConstraint(ModelVisitor::kCumulative, this);
    visitor->VisitIntervalArrayArgument(ModelVisitor::kIntervalsArgument,
                                        intervals_);
    visitor->VisitIntegerVariableArrayArgument(ModelVisitor::kDemandsArgument,
                                               demands_);
    visitor->VisitIntegerExpressionArgument(ModelVisitor::kCapacityArgument,
                                            capacity_);
    visitor->EndVisitConstraint(ModelVisitor::kCumulative, this);
  }
 
  std::string DebugString() const override {
    return absl::StrFormat("VariableDemandCumulativeConstraint([%s], %s)",
                           JoinDebugString(tasks_, ", "),
                           capacity_->DebugString());
  }
 
 private:
  // Post temporal disjunctions for tasks that cannot overlap.
  void PostAllDisjunctions() {
    for (int i = 0; i < intervals_.size(); ++i) {
      IntervalVar* const interval_i = intervals_[i];
      if (interval_i->MayBePerformed()) {
        for (int j = i + 1; j < intervals_.size(); ++j) {
          IntervalVar* const interval_j = intervals_[j];
          if (interval_j->MayBePerformed()) {
            if (CapAdd(tasks_[i].demand->Min(), tasks_[j].demand->Min()) >
                capacity_->Max()) {
              Constraint* const constraint =
                  solver()->MakeTemporalDisjunction(interval_i, interval_j);
              solver()->AddConstraint(constraint);
            }
          }
        }
      }
    }
  }
 
  // Post a Sequence constraint for tasks that requires strictly more than half
  // of the resource
  void PostHighDemandSequenceConstraint() {
    Constraint* constraint = nullptr;
    {  // Need a block to avoid memory leaks in case the AddConstraint fails
      std::vector<IntervalVar*> high_demand_intervals;
      high_demand_intervals.reserve(intervals_.size());
      for (int i = 0; i < demands_.size(); ++i) {
        const int64 demand = tasks_[i].demand->Min();
        // Consider two tasks with demand d1 and d2 such that
        // d1 * 2 > capacity_ and d2 * 2 > capacity_.
        // Then d1 + d2 = 1/2 (d1 * 2 + d2 * 2)
        //              > 1/2 (capacity_ + capacity_)
        //              > capacity_.
        // Therefore these two tasks cannot overlap.
        if (demand * 2 > capacity_->Max() &&
            tasks_[i].interval->MayBePerformed()) {
          high_demand_intervals.push_back(tasks_[i].interval);
        }
      }
      if (high_demand_intervals.size() >= 2) {
        // If there are less than 2 such intervals, the constraint would do
        // nothing
        const std::string seq_name =
            absl::StrCat(name(), "-HighDemandSequence");
        constraint = solver()->MakeStrictDisjunctiveConstraint(
            high_demand_intervals, seq_name);
      }
    }
    if (constraint != nullptr) {
      solver()->AddConstraint(constraint);
    }
  }
 
  // Populates the given vector with useful tasks, meaning the ones on which
  // some propagation can be done
  void PopulateVectorUsefulTasks(
      bool mirror, std::vector<VariableCumulativeTask*>* const useful_tasks) {
    DCHECK(useful_tasks->empty());
    for (int i = 0; i < tasks_.size(); ++i) {
      const VariableCumulativeTask& original_task = tasks_[i];
      IntervalVar* const interval = original_task.interval;
      // Check if exceed capacity
      if (original_task.demand->Min() > capacity_->Max()) {
        interval->SetPerformed(false);
      }
      // Add to the useful_task vector if it may be performed and that it
      // may actually consume some of the resource.
      if (interval->MayBePerformed() && original_task.demand->Max() > 0) {
        Solver* const s = solver();
        IntervalVar* const original_interval = original_task.interval;
        IntervalVar* const interval =
            mirror ? s->MakeMirrorInterval(original_interval)
                   : original_interval;
        IntervalVar* const relaxed_max = s->MakeIntervalRelaxedMax(interval);
        useful_tasks->push_back(
            new VariableCumulativeTask(relaxed_max, original_task.demand));
      }
    }
  }
 
  // Makes and returns an edge-finder or a time table, or nullptr if it is not
  // necessary.
  Constraint* MakeOneSidedConstraint(bool mirror, bool edge_finder,
                                     bool tt_sync) {
    std::vector<VariableCumulativeTask*> useful_tasks;
    PopulateVectorUsefulTasks(mirror, &useful_tasks);
    if (useful_tasks.empty()) {
      return nullptr;
    } else {
      Solver* const s = solver();
      if (edge_finder) {
        return s->RevAlloc(
            new EdgeFinder<VariableCumulativeTask>(s, useful_tasks, capacity_));
      }
      if (tt_sync) {
        return s->RevAlloc(new TimeTableSync<VariableCumulativeTask>(
            s, useful_tasks, capacity_));
      }
      return s->RevAlloc(new CumulativeTimeTable<VariableCumulativeTask>(
          s, useful_tasks, capacity_));
    }
  }
 
  // Post a straight or mirrored edge-finder, if needed
  void PostOneSidedConstraint(bool mirror, bool edge_finder, bool tt_sync) {
    Constraint* const constraint =
        MakeOneSidedConstraint(mirror, edge_finder, tt_sync);
    if (constraint != nullptr) {
      solver()->AddConstraint(constraint);
    }
  }
 
  // Capacity of the cumulative resource
  IntVar* const capacity_;
 
  // The tasks that share the cumulative resource
  std::vector<VariableCumulativeTask> tasks_;
 
  // Array of intervals for the visitor.
  const std::vector<IntervalVar*> intervals_;
  // Array of demands for the visitor.
  const std::vector<IntVar*> demands_;
 
  DISALLOW_COPY_AND_ASSIGN(VariableDemandCumulativeConstraint);
};
}  // namespace
 
// Sequence Constraint
 
// ----- Public class -----
 
DisjunctiveConstraint::DisjunctiveConstraint(
    Solver* const s, const std::vector<IntervalVar*>& intervals,
    const std::string& name)
    : Constraint(s), intervals_(intervals) {
  if (!name.empty()) {
    set_name(name);
  }
  transition_time_ = [](int64 x, int64 y) { return 0; };
}
 
DisjunctiveConstraint::~DisjunctiveConstraint() {}
 
void DisjunctiveConstraint::SetTransitionTime(
    std::function<int64(int64, int64)> transition_time) {
  if (transition_time != nullptr) {
    transition_time_ = transition_time;
  } else {
    transition_time_ = [](int64 x, int64 y) { return 0; };
  }
}
 
// ---------- Factory methods ----------
 
DisjunctiveConstraint* Solver::MakeDisjunctiveConstraint(
    const std::vector<IntervalVar*>& intervals, const std::string& name) {
  return RevAlloc(new FullDisjunctiveConstraint(this, intervals, name, false));
}
 
DisjunctiveConstraint* Solver::MakeStrictDisjunctiveConstraint(
    const std::vector<IntervalVar*>& intervals, const std::string& name) {
  return RevAlloc(new FullDisjunctiveConstraint(this, intervals, name, true));
}
 
// Demands are constant
 
Constraint* Solver::MakeCumulative(const std::vector<IntervalVar*>& intervals,
                                   const std::vector<int64>& demands,
                                   int64 capacity, const std::string& name) {
  CHECK_EQ(intervals.size(), demands.size());
  for (int i = 0; i < intervals.size(); ++i) {
    CHECK_GE(demands[i], 0);
  }
  if (capacity == 1 && AreAllOnes(demands)) {
    return MakeDisjunctiveConstraint(intervals, name);
  }
  return RevAlloc(new CumulativeConstraint(this, intervals, demands,
                                           MakeIntConst(capacity), name));
}
 
Constraint* Solver::MakeCumulative(const std::vector<IntervalVar*>& intervals,
                                   const std::vector<int>& demands,
                                   int64 capacity, const std::string& name) {
  return MakeCumulative(intervals, ToInt64Vector(demands), capacity, name);
}
 
Constraint* Solver::MakeCumulative(const std::vector<IntervalVar*>& intervals,
                                   const std::vector<int64>& demands,
                                   IntVar* const capacity,
                                   const std::string& name) {
  CHECK_EQ(intervals.size(), demands.size());
  for (int i = 0; i < intervals.size(); ++i) {
    CHECK_GE(demands[i], 0);
  }
  return RevAlloc(
      new CumulativeConstraint(this, intervals, demands, capacity, name));
}
 
Constraint* Solver::MakeCumulative(const std::vector<IntervalVar*>& intervals,
                                   const std::vector<int>& demands,
                                   IntVar* const capacity,
                                   const std::string& name) {
  return MakeCumulative(intervals, ToInt64Vector(demands), capacity, name);
}
 
// Demands are variable
 
Constraint* Solver::MakeCumulative(const std::vector<IntervalVar*>& intervals,
                                   const std::vector<IntVar*>& demands,
                                   int64 capacity, const std::string& name) {
  CHECK_EQ(intervals.size(), demands.size());
  for (int i = 0; i < intervals.size(); ++i) {
    CHECK_GE(demands[i]->Min(), 0);
  }
  if (AreAllBound(demands)) {
    std::vector<int64> fixed_demands(demands.size());
    for (int i = 0; i < demands.size(); ++i) {
      fixed_demands[i] = demands[i]->Value();
    }
    return MakeCumulative(intervals, fixed_demands, capacity, name);
  }
  return RevAlloc(new VariableDemandCumulativeConstraint(
      this, intervals, demands, MakeIntConst(capacity), name));
}
 
Constraint* Solver::MakeCumulative(const std::vector<IntervalVar*>& intervals,
                                   const std::vector<IntVar*>& demands,
                                   IntVar* const capacity,
                                   const std::string& name) {
  CHECK_EQ(intervals.size(), demands.size());
  for (int i = 0; i < intervals.size(); ++i) {
    CHECK_GE(demands[i]->Min(), 0);
  }
  if (AreAllBound(demands)) {
    std::vector<int64> fixed_demands(demands.size());
    for (int i = 0; i < demands.size(); ++i) {
      fixed_demands[i] = demands[i]->Value();
    }
    return MakeCumulative(intervals, fixed_demands, capacity, name);
  }
  return RevAlloc(new VariableDemandCumulativeConstraint(
      this, intervals, demands, capacity, name));
}
}  // namespace operations_research