ortools-clone/ortools/constraint_solver/routing_lp_scheduling.h

// Copyright 2010-2025 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.

#ifndef ORTOOLS_CONSTRAINT_SOLVER_ROUTING_LP_SCHEDULING_H_
#define ORTOOLS_CONSTRAINT_SOLVER_ROUTING_LP_SCHEDULING_H_

#include <algorithm>
#include <cstdint>
#include <deque>
#include <functional>
#include <limits>
#include <memory>
#include <ostream>
#include <string>
#include <utility>
#include <vector>

#include "absl/container/flat_hash_map.h"
#include "absl/container/flat_hash_set.h"
#include "absl/log/check.h"
#include "absl/strings/str_format.h"
#include "absl/strings/string_view.h"
#include "absl/time/time.h"
#include "absl/types/span.h"
#include "ortools/base/logging.h"
#include "ortools/base/mathutil.h"
#include "ortools/constraint_solver/routing.h"
#include "ortools/constraint_solver/routing_parameters.pb.h"
#include "ortools/glop/lp_solver.h"
#include "ortools/glop/parameters.pb.h"
#include "ortools/lp_data/lp_data.h"
#include "ortools/lp_data/lp_types.h"
#include "ortools/port/proto_utils.h"
#include "ortools/sat/cp_model.pb.h"
#include "ortools/sat/cp_model_solver.h"
#include "ortools/sat/model.h"
#include "ortools/sat/sat_parameters.pb.h"
#include "ortools/util/piecewise_linear_function.h"
#include "ortools/util/sorted_interval_list.h"

namespace operations_research {

// Classes to solve dimension cumul placement (aka scheduling) problems using
// linear programming.

// Utility class used in the core optimizer to tighten the cumul bounds as much
// as possible based on the model precedences.
class CumulBoundsPropagator {
 public:
  explicit CumulBoundsPropagator(const RoutingDimension* dimension);

  // Tightens the cumul bounds starting from the current cumul var min/max,
  // and propagating the precedences resulting from the next_accessor, and the
  // dimension's precedence rules.
  // Returns false iff the precedences are infeasible with the given routes.
  // Otherwise, the user can call CumulMin() and CumulMax() to retrieve the new
  // bounds of an index.
  bool PropagateCumulBounds(
      const std::function<int64_t(int64_t)>& next_accessor,
      int64_t cumul_offset,
      const std::vector<RoutingModel::RouteDimensionTravelInfo>*
          dimension_travel_info_per_route = nullptr);

  int64_t CumulMin(int index) const {
    return propagated_bounds_[PositiveNode(index)];
  }

  int64_t CumulMax(int index) const {
    const int64_t negated_upper_bound = propagated_bounds_[NegativeNode(index)];
    return negated_upper_bound == std::numeric_limits<int64_t>::min()
               ? std::numeric_limits<int64_t>::max()
               : -negated_upper_bound;
  }

  const RoutingDimension& dimension() const { return dimension_; }

 private:
  // An arc "tail --offset--> head" represents the relation
  // tail + offset <= head.
  // As arcs are stored by tail, we don't store it in the struct.
  struct ArcInfo {
    int head;
    int64_t offset;
  };
  static const int kNoParent;
  static const int kParentToBePropagated;

  // Return the node corresponding to the lower bound of the cumul of index and
  // -index respectively.
  int PositiveNode(int index) const { return 2 * index; }
  int NegativeNode(int index) const { return 2 * index + 1; }

  void AddNodeToQueue(int node) {
    if (!node_in_queue_[node]) {
      bf_queue_.push_back(node);
      node_in_queue_[node] = true;
    }
  }

  // Adds the relation first_index + offset <= second_index, by adding arcs
  // first_index --offset--> second_index and
  // -second_index --offset--> -first_index.
  void AddArcs(int first_index, int second_index, int64_t offset);

  bool InitializeArcsAndBounds(
      const std::function<int64_t(int64_t)>& next_accessor,
      int64_t cumul_offset,
      const std::vector<RoutingModel::RouteDimensionTravelInfo>*
          dimension_travel_info_per_route = nullptr);

  bool UpdateCurrentLowerBoundOfNode(int node, int64_t new_lb, int64_t offset);

  bool DisassembleSubtree(int source, int target);

  bool CleanupAndReturnFalse() {
    // We clean-up node_in_queue_ for future calls, and return false.
    for (int node_to_cleanup : bf_queue_) {
      node_in_queue_[node_to_cleanup] = false;
    }
    bf_queue_.clear();
    return false;
  }

  const RoutingDimension& dimension_;
  const int64_t num_nodes_;

  // TODO(user): Investigate if all arcs for a given tail can be created
  // at the same time, in which case outgoing_arcs_ could point to an absl::Span
  // for each tail index.
  std::vector<std::vector<ArcInfo>> outgoing_arcs_;

  std::deque<int> bf_queue_;
  std::vector<bool> node_in_queue_;
  std::vector<int> tree_parent_node_of_;
  // After calling PropagateCumulBounds(), for each node index n,
  // propagated_bounds_[2*n] and -propagated_bounds_[2*n+1] respectively contain
  // the propagated lower and upper bounds of n's cumul variable.
  std::vector<int64_t> propagated_bounds_;

  // Vector used in DisassembleSubtree() to avoid memory reallocation.
  std::vector<int> tmp_dfs_stack_;

  // Used to store the pickup/delivery pairs encountered on the routes.
  std::vector<std::pair<int64_t, int64_t>>
      visited_pickup_delivery_indices_for_pair_;
};

enum class DimensionSchedulingStatus {
  // An optimal solution was found respecting all constraints.
  OPTIMAL,
  // An optimal solution was found, however constraints which were relaxed were
  // violated.
  RELAXED_OPTIMAL_ONLY,
  // Only a feasible solution was found, optimality was not proven.
  FEASIBLE,
  // A solution could not be found.
  INFEASIBLE
};

class RoutingLinearSolverWrapper {
 public:
  static const int kNoConstraint = -1;

  explicit RoutingLinearSolverWrapper(RoutingSearchStats* search_stats)
      : search_stats_(search_stats) {}
  virtual ~RoutingLinearSolverWrapper() = default;
  virtual void Clear() = 0;
  virtual int CreateNewPositiveVariable() = 0;
  virtual void SetVariableName(int index, absl::string_view name) = 0;
  virtual bool SetVariableBounds(int index, int64_t lower_bound,
                                 int64_t upper_bound) = 0;
  virtual void SetVariableDisjointBounds(int index,
                                         const std::vector<int64_t>& starts,
                                         const std::vector<int64_t>& ends) = 0;
  virtual int64_t GetVariableLowerBound(int index) const = 0;
  virtual int64_t GetVariableUpperBound(int index) const = 0;
  virtual void SetObjectiveCoefficient(int index, double coefficient) = 0;
  virtual double GetObjectiveCoefficient(int index) const = 0;
  virtual void ClearObjective() = 0;
  virtual int NumVariables() const = 0;
  virtual int CreateNewConstraint(int64_t lower_bound, int64_t upper_bound) = 0;
  virtual void SetCoefficient(int ct, int index, double coefficient) = 0;
  virtual bool IsCPSATSolver() = 0;
  virtual void AddObjectiveConstraint() = 0;
  virtual void AddMaximumConstraint(int max_var, std::vector<int> vars) = 0;
  virtual void AddProductConstraint(int product_var, std::vector<int> vars) = 0;
  virtual void SetEnforcementLiteral(int ct, int condition) = 0;
  virtual DimensionSchedulingStatus Solve(absl::Duration duration_limit) = 0;
  virtual int64_t GetObjectiveValue() const = 0;
  virtual int64_t GetVariableValue(int index) const = 0;
  virtual bool SolutionIsInteger() const = 0;

  // This function is meant to override the parameters of the solver.
  virtual void SetParameters(const std::string& parameters) = 0;
  // When solving a scheduling problem, this can be called to add hints that
  // help the underlying solver:
  // - nodes is the sequence of nodes in a route represented in the model.
  // - schedule_variables is the sequence of variables used to represent the
  //   time at which each node is scheduled.
  virtual void AddRoute(absl::Span<const int64_t> nodes,
                        absl::Span<const int> schedule_variables) = 0;

  // Returns if the model is empty or not.
  virtual bool ModelIsEmpty() const { return true; }

  // Prints an understandable view of the model.
  virtual std::string PrintModel() const = 0;

  // Adds a variable with bounds [lower_bound, upper_bound].
  int AddVariable(int64_t lower_bound, int64_t upper_bound) {
    CHECK_LE(lower_bound, upper_bound);
    const int variable = CreateNewPositiveVariable();
    SetVariableBounds(variable, lower_bound, upper_bound);
    return variable;
  }
  // Adds a linear constraint, enforcing
  // lower_bound <= sum variable * coeff <= upper_bound,
  // and returns the identifier of that constraint.
  int AddLinearConstraint(
      int64_t lower_bound, int64_t upper_bound,
      absl::Span<const std::pair<int, double>> variable_coeffs) {
    CHECK_LE(lower_bound, upper_bound);
    const int ct = CreateNewConstraint(lower_bound, upper_bound);
    for (const auto& variable_coeff : variable_coeffs) {
      SetCoefficient(ct, variable_coeff.first, variable_coeff.second);
    }
    return ct;
  }
  // Adds a linear constraint and a 0/1 variable that is true iff
  // lower_bound <= sum variable * coeff <= upper_bound,
  // and returns the identifier of that variable.
  int AddReifiedLinearConstraint(
      int64_t lower_bound, int64_t upper_bound,
      absl::Span<const std::pair<int, double>> weighted_variables) {
    const int reification_ct = AddLinearConstraint(1, 1, {});
    if (std::numeric_limits<int64_t>::min() < lower_bound) {
      const int under_lower_bound = AddVariable(0, 1);
#ifndef NDEBUG
      SetVariableName(under_lower_bound, "under_lower_bound");
#endif
      SetCoefficient(reification_ct, under_lower_bound, 1);
      const int under_lower_bound_ct =
          AddLinearConstraint(std::numeric_limits<int64_t>::min(),
                              lower_bound - 1, weighted_variables);
      SetEnforcementLiteral(under_lower_bound_ct, under_lower_bound);
    }
    if (upper_bound < std::numeric_limits<int64_t>::max()) {
      const int above_upper_bound = AddVariable(0, 1);
#ifndef NDEBUG
      SetVariableName(above_upper_bound, "above_upper_bound");
#endif
      SetCoefficient(reification_ct, above_upper_bound, 1);
      const int above_upper_bound_ct = AddLinearConstraint(
          upper_bound + 1, std::numeric_limits<int64_t>::max(),
          weighted_variables);
      SetEnforcementLiteral(above_upper_bound_ct, above_upper_bound);
    }
    const int within_bounds = AddVariable(0, 1);
#ifndef NDEBUG
    SetVariableName(within_bounds, "within_bounds");
#endif
    SetCoefficient(reification_ct, within_bounds, 1);
    const int within_bounds_ct =
        AddLinearConstraint(lower_bound, upper_bound, weighted_variables);
    SetEnforcementLiteral(within_bounds_ct, within_bounds);
    return within_bounds;
  }

 protected:
  RoutingSearchStats* const search_stats_;
};

class RoutingGlopWrapper : public RoutingLinearSolverWrapper {
 public:
  RoutingGlopWrapper(bool is_relaxation, const glop::GlopParameters& parameters,
                     RoutingSearchStats* search_stats)
      : RoutingLinearSolverWrapper(search_stats),
        is_relaxation_(is_relaxation) {
    lp_solver_.SetParameters(parameters);
    linear_program_.SetMaximizationProblem(false);
  }
  void Clear() override {
    linear_program_.Clear();
    linear_program_.SetMaximizationProblem(false);
    allowed_intervals_.clear();
  }
  int CreateNewPositiveVariable() override {
    return linear_program_.CreateNewVariable().value();
  }
  void SetVariableName(int index, absl::string_view name) override {
    linear_program_.SetVariableName(glop::ColIndex(index), name);
  }
  bool SetVariableBounds(int index, int64_t lower_bound,
                         int64_t upper_bound) override {
    DCHECK_GE(lower_bound, 0);
    // When variable upper bounds are greater than this threshold, precision
    // issues arise in GLOP. In this case we are just going to suppose that
    // these high bound values are infinite and not set the upper bound.
    const int64_t kMaxValue = 1e10;
    const double lp_min = lower_bound;
    const double lp_max =
        (upper_bound > kMaxValue) ? glop::kInfinity : upper_bound;
    if (lp_min <= lp_max) {
      linear_program_.SetVariableBounds(glop::ColIndex(index), lp_min, lp_max);
      return true;
    }
    // The linear_program would not be feasible, and it cannot handle the
    // lp_min > lp_max case, so we must detect infeasibility here.
    return false;
  }
  void SetVariableDisjointBounds(int index, const std::vector<int64_t>& starts,
                                 const std::vector<int64_t>& ends) override {
    // TODO(user): Investigate if we can avoid rebuilding the interval list
    // each time (we could keep a reference to the forbidden interval list in
    // RoutingDimension but we would need to store cumul offsets and use them
    // when checking intervals).
    allowed_intervals_[index] =
        std::make_unique<SortedDisjointIntervalList>(starts, ends);
  }
  int64_t GetVariableLowerBound(int index) const override {
    return linear_program_.variable_lower_bounds()[glop::ColIndex(index)];
  }
  int64_t GetVariableUpperBound(int index) const override {
    const double upper_bound =
        linear_program_.variable_upper_bounds()[glop::ColIndex(index)];
    DCHECK_GE(upper_bound, 0);
    return upper_bound == glop::kInfinity ? std::numeric_limits<int64_t>::max()
                                          : static_cast<int64_t>(upper_bound);
  }
  void SetObjectiveCoefficient(int index, double coefficient) override {
    linear_program_.SetObjectiveCoefficient(glop::ColIndex(index), coefficient);
  }
  double GetObjectiveCoefficient(int index) const override {
    return linear_program_.objective_coefficients()[glop::ColIndex(index)];
  }
  void ClearObjective() override {
    for (glop::ColIndex i(0); i < linear_program_.num_variables(); ++i) {
      linear_program_.SetObjectiveCoefficient(i, 0);
    }
  }
  int NumVariables() const override {
    return linear_program_.num_variables().value();
  }
  int CreateNewConstraint(int64_t lower_bound, int64_t upper_bound) override {
    const glop::RowIndex ct = linear_program_.CreateNewConstraint();
    linear_program_.SetConstraintBounds(
        ct,
        (lower_bound == std::numeric_limits<int64_t>::min()) ? -glop::kInfinity
                                                             : lower_bound,
        (upper_bound == std::numeric_limits<int64_t>::max()) ? glop::kInfinity
                                                             : upper_bound);
    return ct.value();
  }
  void SetCoefficient(int ct, int index, double coefficient) override {
    // Necessary to keep the model clean
    // (cf. glop::LinearProgram::NotifyThatColumnsAreClean).
    if (coefficient == 0.0) return;
    linear_program_.SetCoefficient(glop::RowIndex(ct), glop::ColIndex(index),
                                   coefficient);
  }
  bool IsCPSATSolver() override { return false; }
  void AddObjectiveConstraint() override {
    double max_coefficient = 0;
    for (int variable = 0; variable < NumVariables(); variable++) {
      const double coefficient = GetObjectiveCoefficient(variable);
      max_coefficient = std::max(MathUtil::Abs(coefficient), max_coefficient);
    }
    DCHECK_GE(max_coefficient, 0);
    if (max_coefficient == 0) {
      // There are no terms in the objective.
      return;
    }
    const glop::RowIndex ct = linear_program_.CreateNewConstraint();
    double normalized_objective_value = 0;
    for (int variable = 0; variable < NumVariables(); variable++) {
      const double coefficient = GetObjectiveCoefficient(variable);
      if (coefficient != 0) {
        const double normalized_coeff = coefficient / max_coefficient;
        SetCoefficient(ct.value(), variable, normalized_coeff);
        normalized_objective_value +=
            normalized_coeff * GetValueDouble(glop::ColIndex(variable));
      }
    }
    normalized_objective_value = std::max(
        normalized_objective_value, GetObjectiveValue() / max_coefficient);
    linear_program_.SetConstraintBounds(ct, -glop::kInfinity,
                                        normalized_objective_value);
  }
  void AddMaximumConstraint(int /*max_var*/,
                            std::vector<int> /*vars*/) override {}
  void AddProductConstraint(int /*product_var*/,
                            std::vector<int> /*vars*/) override {}
  void SetEnforcementLiteral(int /*ct*/, int /*condition*/) override {};
  void AddRoute(absl::Span<const int64_t>, absl::Span<const int>) override{};
  DimensionSchedulingStatus Solve(absl::Duration duration_limit) override {
    lp_solver_.GetMutableParameters()->set_max_time_in_seconds(
        absl::ToDoubleSeconds(duration_limit));

    // Because we construct the lp one constraint at a time and we never call
    // SetCoefficient() on the same variable twice for a constraint, we know
    // that the columns do not contain duplicates and are already ordered by
    // constraint so we do not need to call linear_program->CleanUp() which can
    // be costly. Note that the assumptions are DCHECKed() in the call below.
    linear_program_.NotifyThatColumnsAreClean();
    VLOG(2) << linear_program_.Dump();
    const glop::ProblemStatus status = lp_solver_.Solve(linear_program_);
    if (search_stats_) search_stats_->num_glop_calls_in_lp_scheduling++;
    const bool feasible_only = status == glop::ProblemStatus::PRIMAL_FEASIBLE;
    if (status != glop::ProblemStatus::OPTIMAL &&
        status != glop::ProblemStatus::IMPRECISE && !feasible_only) {
      return DimensionSchedulingStatus::INFEASIBLE;
    }
    if (is_relaxation_) {
      return DimensionSchedulingStatus::RELAXED_OPTIMAL_ONLY;
    }
    for (const auto& allowed_interval : allowed_intervals_) {
      const int64_t value = GetVariableValue(allowed_interval.first);
      const SortedDisjointIntervalList* const interval_list =
          allowed_interval.second.get();
      const auto it = interval_list->FirstIntervalGreaterOrEqual(value);
      if (it == interval_list->end() || value < it->start) {
        return DimensionSchedulingStatus::RELAXED_OPTIMAL_ONLY;
      }
    }
    if (feasible_only && !linear_program_.objective_coefficients().empty()) {
      return DimensionSchedulingStatus::FEASIBLE;
    }
    return DimensionSchedulingStatus::OPTIMAL;
  }
  int64_t GetObjectiveValue() const override {
    return MathUtil::Round<int64_t>(lp_solver_.GetObjectiveValue());
  }
  int64_t GetVariableValue(int index) const override {
    const double value_double = GetValueDouble(glop::ColIndex(index));
    return (value_double >= std::numeric_limits<int64_t>::max())
               ? std::numeric_limits<int64_t>::max()
               : MathUtil::Round<int64_t>(value_double);
  }
  bool SolutionIsInteger() const override {
    return linear_program_.SolutionIsInteger(lp_solver_.variable_values(),
                                             /*absolute_tolerance*/ 1e-3);
  }

  void SetParameters(const std::string& parameters) override {
    glop::GlopParameters params;
    const bool status = params.ParseFromString(parameters);
    DCHECK(status);
    lp_solver_.SetParameters(params);
  }

  // Prints an understandable view of the model
  // TODO(user): Improve output readability.
  std::string PrintModel() const override { return linear_program_.Dump(); }

 private:
  double GetValueDouble(glop::ColIndex index) const {
    return lp_solver_.variable_values()[index];
  }

  const bool is_relaxation_;
  glop::LinearProgram linear_program_;
  glop::LPSolver lp_solver_;
  absl::flat_hash_map<int, std::unique_ptr<SortedDisjointIntervalList>>
      allowed_intervals_;
};

class RoutingCPSatWrapper : public RoutingLinearSolverWrapper {
 public:
  explicit RoutingCPSatWrapper(RoutingSearchStats* const search_stats)
      : RoutingLinearSolverWrapper(search_stats) {
    parameters_.set_num_workers(1);
    // Keeping presolve but with 1 iteration; as of 10/2023 it is
    // significantly faster than both full presolve and no presolve.
    parameters_.set_cp_model_presolve(true);
    parameters_.set_max_presolve_iterations(1);
    parameters_.set_cp_model_probing_level(0);
    parameters_.set_use_sat_inprocessing(false);
    parameters_.set_symmetry_level(0);
    parameters_.set_catch_sigint_signal(false);
    parameters_.set_mip_max_bound(1e8);
    parameters_.set_search_branching(sat::SatParameters::PORTFOLIO_SEARCH);
    parameters_.set_linearization_level(2);
    parameters_.set_cut_level(0);
    parameters_.set_add_lp_constraints_lazily(false);
    parameters_.set_use_absl_random(false);
    parameters_.set_alternative_pool_size(0);
    parameters_.set_transitive_precedences_work_limit(0);
  }
  ~RoutingCPSatWrapper() override {}
  void Clear() override {
    model_.Clear();
    response_.Clear();
    objective_coefficients_.clear();
    schedule_variables_.clear();
  }
  int CreateNewPositiveVariable() override {
    const int index = model_.variables_size();
    sat::IntegerVariableProto* const variable = model_.add_variables();
    variable->add_domain(0);
    variable->add_domain(static_cast<int64_t>(parameters_.mip_max_bound()));
    return index;
  }
  void SetVariableName(int index, absl::string_view name) override {
    model_.mutable_variables(index)->set_name(name.data());
  }
  bool SetVariableBounds(int index, int64_t lower_bound,
                         int64_t upper_bound) override {
    DCHECK_GE(lower_bound, 0);
    const int64_t capped_upper_bound =
        std::min<int64_t>(upper_bound, parameters_.mip_max_bound());
    if (lower_bound > capped_upper_bound) return false;
    sat::IntegerVariableProto* const variable = model_.mutable_variables(index);
    variable->set_domain(0, lower_bound);
    variable->set_domain(1, capped_upper_bound);
    return true;
  }
  void SetVariableDisjointBounds(int index, const std::vector<int64_t>& starts,
                                 const std::vector<int64_t>& ends) override {
    DCHECK_EQ(starts.size(), ends.size());
    const int ct = CreateNewConstraint(1, 1);
    for (int i = 0; i < starts.size(); ++i) {
      const int variable = CreateNewPositiveVariable();
#ifndef NDEBUG
      SetVariableName(variable,
                      absl::StrFormat("disjoint(%ld, %ld)", index, i));
#endif
      SetVariableBounds(variable, 0, 1);
      SetCoefficient(ct, variable, 1);
      const int window_ct = CreateNewConstraint(starts[i], ends[i]);
      SetCoefficient(window_ct, index, 1);
      model_.mutable_constraints(window_ct)->add_enforcement_literal(variable);
    }
  }
  int64_t GetVariableLowerBound(int index) const override {
    return model_.variables(index).domain(0);
  }
  int64_t GetVariableUpperBound(int index) const override {
    const auto& domain = model_.variables(index).domain();
    return domain[domain.size() - 1];
  }
  void SetObjectiveCoefficient(int index, double coefficient) override {
    if (index >= objective_coefficients_.size()) {
      objective_coefficients_.resize(index + 1, 0);
    }
    objective_coefficients_[index] = coefficient;
    sat::FloatObjectiveProto* const objective =
        model_.mutable_floating_point_objective();
    objective->add_vars(index);
    objective->add_coeffs(coefficient);
  }
  double GetObjectiveCoefficient(int index) const override {
    return (index < objective_coefficients_.size())
               ? objective_coefficients_[index]
               : 0;
  }
  void ClearObjective() override {
    model_.mutable_floating_point_objective()->Clear();
  }
  int NumVariables() const override { return model_.variables_size(); }
  int CreateNewConstraint(int64_t lower_bound, int64_t upper_bound) override {
    sat::LinearConstraintProto* const ct =
        model_.add_constraints()->mutable_linear();
    ct->add_domain(lower_bound);
    ct->add_domain(upper_bound);
    return model_.constraints_size() - 1;
  }
  void SetCoefficient(int ct_index, int index, double coefficient) override {
    sat::LinearConstraintProto* const ct =
        model_.mutable_constraints(ct_index)->mutable_linear();
    ct->add_vars(index);
    const int64_t integer_coefficient = coefficient;
    ct->add_coeffs(integer_coefficient);
  }
  bool IsCPSATSolver() override { return true; }
  void AddObjectiveConstraint() override {
    const sat::CpObjectiveProto& objective = response_.integer_objective();
    int64_t activity = 0;
    for (int i = 0; i < objective.vars_size(); ++i) {
      activity += response_.solution(objective.vars(i)) * objective.coeffs(i);
    }
    const int ct =
        CreateNewConstraint(std::numeric_limits<int64_t>::min(), activity);
    for (int i = 0; i < objective.vars_size(); ++i) {
      SetCoefficient(ct, objective.vars(i), objective.coeffs(i));
    }
    model_.clear_objective();
  }
  void AddMaximumConstraint(int max_var, std::vector<int> vars) override {
    sat::LinearArgumentProto* const ct =
        model_.add_constraints()->mutable_lin_max();
    ct->mutable_target()->add_vars(max_var);
    ct->mutable_target()->add_coeffs(1);
    for (const int var : vars) {
      sat::LinearExpressionProto* const expr = ct->add_exprs();
      expr->add_vars(var);
      expr->add_coeffs(1);
    }
  }
  void AddProductConstraint(int product_var, std::vector<int> vars) override {
    sat::LinearArgumentProto* const ct =
        model_.add_constraints()->mutable_int_prod();
    ct->mutable_target()->add_vars(product_var);
    ct->mutable_target()->add_coeffs(1);
    for (const int var : vars) {
      sat::LinearExpressionProto* expr = ct->add_exprs();
      expr->add_vars(var);
      expr->add_coeffs(1);
    }
  }
  void SetEnforcementLiteral(int ct, int condition) override {
    DCHECK_LT(ct, model_.constraints_size());
    model_.mutable_constraints(ct)->add_enforcement_literal(condition);
  }
  void AddRoute(absl::Span<const int64_t> nodes,
                absl::Span<const int> schedule_variables) override {
    DCHECK_EQ(nodes.size(), schedule_variables.size());
    for (const int64_t node : nodes) {
      if (node >= schedule_hint_.size()) schedule_hint_.resize(node + 1, 0);
    }
    for (int n = 0; n < nodes.size(); ++n) {
      schedule_variables_.push_back(
          {.node = nodes[n], .cumul = schedule_variables[n]});
    }
  }
  DimensionSchedulingStatus Solve(absl::Duration duration_limit) override {
    const double max_time = absl::ToDoubleSeconds(duration_limit);
    if (max_time <= 0.0) return DimensionSchedulingStatus::INFEASIBLE;
    parameters_.set_max_time_in_seconds(max_time);
    VLOG(2) << ProtobufDebugString(model_);
    auto record_hint = [this]() {
      hint_.Clear();
      for (int i = 0; i < response_.solution_size(); ++i) {
        hint_.add_vars(i);
        hint_.add_values(response_.solution(i));
      }
      for (const auto& [node, cumul] : schedule_variables_) {
        schedule_hint_[node] = response_.solution(cumul);
      }
    };
    model_.clear_solution_hint();
    auto* hint = model_.mutable_solution_hint();
    if (hint_.vars_size() == model_.variables_size()) {
      *hint = hint_;
    } else {
      for (const auto& [node, cumul] : schedule_variables_) {
        if (schedule_hint_[node] == 0) continue;
        hint->add_vars(cumul);
        hint->add_values(schedule_hint_[node]);
      }
    }
    sat::Model model;
    model.Add(sat::NewSatParameters(parameters_));
    response_ = sat::SolveCpModel(model_, &model);
    if (search_stats_) search_stats_->num_cp_sat_calls_in_lp_scheduling++;
    VLOG(2) << response_;
    DCHECK_NE(response_.status(), sat::CpSolverStatus::MODEL_INVALID);
    if (response_.status() == sat::CpSolverStatus::OPTIMAL ||
        (response_.status() == sat::CpSolverStatus::FEASIBLE &&
         !model_.has_floating_point_objective())) {
      record_hint();
      return DimensionSchedulingStatus::OPTIMAL;
    } else if (response_.status() == sat::CpSolverStatus::FEASIBLE) {
      // TODO(user): Consider storing "feasible" solutions in a separate
      // cache we use as hint when the "optimal" cache is empty.
      return DimensionSchedulingStatus::FEASIBLE;
    }
    return DimensionSchedulingStatus::INFEASIBLE;
  }
  int64_t GetObjectiveValue() const override {
    return MathUtil::Round<int64_t>(response_.objective_value());
  }
  int64_t GetVariableValue(int index) const override {
    return response_.solution(index);
  }
  bool SolutionIsInteger() const override { return true; }

  // NOTE: This function is not implemented for the CP-SAT solver.
  void SetParameters(const std::string& /*parameters*/) override {
    DCHECK(false);
  }

  bool ModelIsEmpty() const override { return model_.ByteSizeLong() == 0; }

  // Prints an understandable view of the model
  std::string PrintModel() const override;

 private:
  sat::CpModelProto model_;
  sat::CpSolverResponse response_;
  sat::SatParameters parameters_;
  std::vector<double> objective_coefficients_;
  sat::PartialVariableAssignment hint_;
  struct NodeAndCumul {
    int64_t node;
    int cumul;
  };
  // Stores node/cumul pairs of the routes in the current model.
  std::vector<NodeAndCumul> schedule_variables_;
  // Maps node to its last known value in any optimal solution.
  std::vector<int64_t> schedule_hint_;
};

// Utility class used in Local/GlobalDimensionCumulOptimizer to set the linear
// solver constraints and solve the problem.
class DimensionCumulOptimizerCore {
  using RouteDimensionTravelInfo = RoutingModel::RouteDimensionTravelInfo;
  using Resource = RoutingModel::ResourceGroup::Resource;

 public:
  DimensionCumulOptimizerCore(const RoutingDimension* dimension,
                              bool use_precedence_propagator);

  // Finds an optimal (or just feasible) solution for the route for the given
  // resource. If the resource is null, it is simply ignored.
  DimensionSchedulingStatus OptimizeSingleRouteWithResource(
      int vehicle, double solve_duration_ratio,
      const std::function<int64_t(int64_t)>& next_accessor,
      const RouteDimensionTravelInfo* dimension_travel_info,
      const Resource* resource, bool optimize_vehicle_costs,
      RoutingLinearSolverWrapper* solver, std::vector<int64_t>* cumul_values,
      std::vector<int64_t>* break_values, int64_t* cost_without_transit,
      int64_t* transit_cost, bool clear_lp = true);

  // Given some cumuls and breaks, computes the solution cost by solving the
  // same model as in OptimizeSingleRouteWithResource() with the addition of
  // constraints for cumuls and breaks.
  DimensionSchedulingStatus ComputeSingleRouteSolutionCostWithoutFixedTransits(
      int vehicle, double solve_duration_ratio,
      const std::function<int64_t(int64_t)>& next_accessor,
      const RouteDimensionTravelInfo* dimension_travel_info,
      RoutingLinearSolverWrapper* solver,
      absl::Span<const int64_t> solution_cumul_values,
      absl::Span<const int64_t> solution_break_values,
      int64_t* cost_without_transits, int64_t* cost_offset = nullptr,
      bool reuse_previous_model_if_possible = true, bool clear_lp = false,
      bool clear_solution_constraints = true,
      absl::Duration* solve_duration = nullptr);

  // Computes the optimal scheduling solution(s) for the route for each resource
  // in 'resources' with an index in 'resource_indices'.
  // If both 'resources' and 'resource_indices' are empty, computes the optimal
  // solution for the route itself (without added resource constraints).
  std::vector<DimensionSchedulingStatus> OptimizeSingleRouteWithResources(
      int vehicle, double solve_duration_ratio,
      const std::function<int64_t(int64_t)>& next_accessor,
      const std::function<int64_t(int64_t, int64_t)>& transit_accessor,
      const RouteDimensionTravelInfo* dimension_travel_info,
      absl::Span<const Resource> resources,
      absl::Span<const int> resource_indices, bool optimize_vehicle_costs,
      RoutingLinearSolverWrapper* solver,
      std::vector<std::vector<int64_t>>* cumul_values,
      std::vector<std::vector<int64_t>>* break_values,
      std::vector<int64_t>* costs_without_transits, int64_t* transit_cost,
      bool clear_lp = true);

  // In the Optimize() method, if both 'cumul_values' and 'cost' parameters are
  // null, we don't optimize the cost and stop at the first feasible solution in
  // the linear solver (since in this case only feasibility is of interest).
  // When 'optimize_resource_assignment' is false, the resource var values are
  // used to constrain the vehicle routes according to their assigned resource.
  DimensionSchedulingStatus Optimize(
      const std::function<int64_t(int64_t)>& next_accessor,
      const std::vector<RouteDimensionTravelInfo>&
          dimension_travel_info_per_route,
      RoutingLinearSolverWrapper* solver, std::vector<int64_t>* cumul_values,
      std::vector<int64_t>* break_values,
      std::vector<std::vector<int>>* resource_indices_per_group,
      int64_t* cost_without_transits, int64_t* transit_cost,
      bool clear_lp = true, bool optimize_resource_assignment = true);

  DimensionSchedulingStatus OptimizeAndPack(
      const std::function<int64_t(int64_t)>& next_accessor,
      const std::vector<RouteDimensionTravelInfo>&
          dimension_travel_info_per_route,
      RoutingLinearSolverWrapper* solver, std::vector<int64_t>* cumul_values,
      std::vector<int64_t>* break_values);

  DimensionSchedulingStatus OptimizeAndPackSingleRoute(
      int vehicle, double solve_duration_ratio,
      const std::function<int64_t(int64_t)>& next_accessor,
      const RouteDimensionTravelInfo* dimension_travel_info,
      const Resource* resource, RoutingLinearSolverWrapper* solver,
      std::vector<int64_t>* cumul_values, std::vector<int64_t>* break_values);

  struct TransitTargetCost {
    double threshold_ratio;
    int64_t cost_coefficient_below_threshold;
    int64_t cost_coefficient_above_threshold;
  };
  DimensionSchedulingStatus OptimizeSingleRouteWithTransitTargets(
      int vehicle, double solve_duration_ratio,
      const std::function<int64_t(int64_t)>& next_accessor,
      absl::Span<const int64_t> transit_targets,
      TransitTargetCost transit_target_cost, RoutingLinearSolverWrapper* solver,
      std::vector<int64_t>* optimal_transits,
      std::vector<int64_t>* optimal_cumuls,
      std::vector<int64_t>* optimal_breaks);

  const RoutingDimension* dimension() const { return dimension_; }

 private:
  // Initializes the containers and given solver. Must be called prior to
  // setting any constraints and solving.
  void InitOptimizer(RoutingLinearSolverWrapper* solver);

  // Computes the minimum/maximum of cumuls for nodes on "route", and sets them
  // in current_route_[min|max]_cumuls_ respectively.
  bool ExtractRouteCumulBounds(absl::Span<const int64_t> route,
                               int64_t cumul_offset);

  // Tighten the minimum/maximum of cumuls for nodes on "route"
  // If the propagator_ is not null, uses the bounds tightened by the
  // propagator. Otherwise, the minimum transits are used to tighten them.
  bool TightenRouteCumulBounds(absl::Span<const int64_t> route,
                               absl::Span<const int64_t> min_transits,
                               int64_t cumul_offset);

  // Sets the constraints for all nodes on "vehicle"'s route according to
  // "next_accessor". If optimize_costs is true, also sets the objective
  // coefficients for the LP.
  // Returns false if some infeasibility was detected, true otherwise.
  bool SetRouteCumulConstraints(
      int vehicle, const std::function<int64_t(int64_t)>& next_accessor,
      const std::function<int64_t(int64_t, int64_t)>& transit_accessor,
      absl::Span<const int64_t> transit_targets,
      const RouteDimensionTravelInfo* dimension_travel_info,
      int64_t cumul_offset, bool optimize_costs,
      RoutingLinearSolverWrapper* solver, int64_t* route_transit_cost,
      int64_t* route_cost_offset);

  // Sets the objective coefficients related to the cumuls and transits of the
  // route in the solver. Supposes that the current_route_cumul_variables_ and
  // current_route_nodes_ have correctly been initialized prior to calling this
  // method.
  void SetRouteCumulCosts(int vehicle, int64_t cumul_offset,
                          int64_t total_fixed_transit,
                          RoutingLinearSolverWrapper* solver,
                          int64_t* route_transit_cost,
                          int64_t* route_cost_offset);

  // Sets the constraints for all variables related to travel. Handles
  // static or time-dependent travel values.
  // Returns false if some infeasibility was detected, true otherwise.
  bool SetRouteTravelConstraints(
      const RouteDimensionTravelInfo* dimension_travel_info,
      absl::Span<const int> lp_slacks, absl::Span<const int64_t> fixed_transits,
      absl::Span<const int64_t> transit_targets,
      RoutingLinearSolverWrapper* solver);

  // Sets the global constraints on the dimension, and adds global objective
  // cost coefficients if optimize_costs is true.
  // NOTE: When called, the call to this function MUST come after
  // SetRouteCumulConstraints() has been called on all routes, so that
  // index_to_cumul_variable_ and min_start/max_end_cumul_ are correctly
  // initialized.
  // Returns false if some infeasibility was detected, true otherwise.
  bool SetGlobalConstraints(
      const std::function<int64_t(int64_t)>& next_accessor,
      int64_t cumul_offset, bool optimize_costs,
      bool optimize_resource_assignment, RoutingLinearSolverWrapper* solver);

  bool SetGlobalConstraintsForResourceAssignment(
      const std::function<int64_t(int64_t)>& next_accessor,
      int64_t cumul_offset, RoutingLinearSolverWrapper* solver);

  void SetValuesFromLP(absl::Span<const int> lp_variables, int64_t offset,
                       int64_t default_value,
                       RoutingLinearSolverWrapper* solver,
                       std::vector<int64_t>* lp_values) const;

  void SetResourceIndices(
      RoutingLinearSolverWrapper* solver,
      std::vector<std::vector<int>>* resource_indices_per_group) const;

  // This function packs the routes of the given vehicles while keeping the cost
  // of the LP lower than its current (supposed optimal) objective value.
  // It does so by setting the current objective variables' coefficient to 0 and
  // setting the coefficient of the route ends to 1, to first minimize the route
  // ends' cumuls, and then maximizes the starts' cumuls without increasing the
  // ends.
  DimensionSchedulingStatus PackRoutes(
      std::vector<int> vehicles, double solve_duration_ratio,
      RoutingLinearSolverWrapper* solver,
      const glop::GlopParameters& packing_parameters);

  std::unique_ptr<CumulBoundsPropagator> propagator_;
  std::vector<int64_t> current_route_min_cumuls_;
  std::vector<int64_t> current_route_max_cumuls_;
  // Stores the nodes on the current route.
  std::vector<int64_t> current_route_nodes_;
  const RoutingDimension* const dimension_;
  // Scheduler variables for current route cumuls and for all nodes cumuls.
  std::vector<int> current_route_cumul_variables_;
  std::vector<int> index_to_cumul_variable_;
  // Scheduler variables for current route transits.
  std::vector<int> current_route_variable_transit_variables_;
  // Scheduler variables for current route breaks and all vehicle breaks.
  // There are two variables for each break: start and end.
  // current_route_break_variables_ has variables corresponding to
  // break[0] start, break[0] end, break[1] start, break[1] end, etc.
  std::vector<int> current_route_break_variables_;
  // Vector all_break_variables contains the break variables of all vehicles,
  // in the same format as current_route_break_variables.
  // It is the concatenation of break variables of vehicles in [0, #vehicles).
  std::vector<int> all_break_variables_;
  // Allows to retrieve break variables of a given vehicle: those go from
  // all_break_variables_[vehicle_to_all_break_variables_offset_[vehicle]] to
  // all_break_variables[vehicle_to_all_break_variables_offset_[vehicle+1]-1].
  std::vector<int> vehicle_to_all_break_variables_offset_;
  // The following vector contains indices of resource-class-to-vehicle
  // assignment variables. For every resource group, stores indices of
  // num_resource_classes*num_vehicles boolean variables indicating whether
  // resource class #rc is assigned to vehicle #v.
  std::vector<std::vector<int>>
      resource_class_to_vehicle_assignment_variables_per_group_;
  // The following vector keeps track of the resources ignored during resource
  // assignment because they're pre-assigned to a specific vehicle.
  std::vector<std::vector<absl::flat_hash_set<int>>>
      resource_class_ignored_resources_per_group_;

  int max_end_cumul_;
  int min_start_cumul_;
  std::vector<std::pair<int64_t, int64_t>>
      visited_pickup_delivery_indices_for_pair_;
};

// Class used to compute optimal values for dimension cumuls of routes,
// minimizing cumul soft lower and upper bound costs, and vehicle span costs of
// a route.
// In its methods, next_accessor is a callback returning the next node of a
// given node on a route.
class LocalDimensionCumulOptimizer {
 public:
  LocalDimensionCumulOptimizer(
      const RoutingDimension* dimension,
      RoutingSearchParameters::SchedulingSolver solver_type,
      RoutingSearchStats* search_stats);

  // If feasible, computes the optimal cost of the route performed by a vehicle,
  // minimizing cumul soft lower and upper bound costs and vehicle span costs,
  // and stores it in "optimal_cost" (if not null).
  // Returns true iff the route respects all constraints.
  DimensionSchedulingStatus ComputeRouteCumulCost(
      int vehicle, double solve_duration_ratio,
      const std::function<int64_t(int64_t)>& next_accessor,
      int64_t* optimal_cost);

  // Same as ComputeRouteCumulCost, but the cost computed does not contain
  // the part of the vehicle span cost due to fixed transits.
  DimensionSchedulingStatus ComputeRouteCumulCostWithoutFixedTransits(
      int vehicle, double solve_duration_ratio,
      const std::function<int64_t(int64_t)>& next_accessor,
      const RoutingModel::ResourceGroup::Resource* resource,
      int64_t* optimal_cost_without_transits);

  std::vector<DimensionSchedulingStatus>
  ComputeRouteCumulCostsForResourcesWithoutFixedTransits(
      int vehicle, double solve_duration_ratio,
      const std::function<int64_t(int64_t)>& next_accessor,
      const std::function<int64_t(int64_t, int64_t)>& transit_accessor,
      absl::Span<const RoutingModel::ResourceGroup::Resource> resources,
      absl::Span<const int> resource_indices, bool optimize_vehicle_costs,
      std::vector<int64_t>* optimal_costs_without_transits,
      std::vector<std::vector<int64_t>>* optimal_cumuls,
      std::vector<std::vector<int64_t>>* optimal_breaks);

  // If feasible, computes the optimal values for cumul and break variables
  // of the route performed by a vehicle, minimizing cumul soft lower, upper
  // bound costs and vehicle span costs, stores them in "optimal_cumuls"
  // (if not null), and optimal_breaks, and returns true.
  // Returns false if the route is not feasible.
  DimensionSchedulingStatus ComputeRouteCumuls(
      int vehicle, double solve_duration_ratio,
      const std::function<int64_t(int64_t)>& next_accessor,
      const RoutingModel::RouteDimensionTravelInfo* dimension_travel_info,
      const RoutingModel::ResourceGroup::Resource* resource,
      std::vector<int64_t>* optimal_cumuls,
      std::vector<int64_t>* optimal_breaks);

  // Simple combination of ComputeRouteCumulCostWithoutFixedTransits() and
  // ComputeRouteCumuls().
  DimensionSchedulingStatus ComputeRouteCumulsAndCostWithoutFixedTransits(
      int vehicle, double solve_duration_ratio,
      const std::function<int64_t(int64_t)>& next_accessor,
      const RoutingModel::RouteDimensionTravelInfo* dimension_travel_info,
      std::vector<int64_t>* optimal_cumuls,
      std::vector<int64_t>* optimal_breaks,
      int64_t* optimal_cost_without_transits);

  // If feasible, computes the cost of a given route performed by a vehicle
  // defined by its cumuls and breaks.
  DimensionSchedulingStatus ComputeRouteSolutionCostWithoutFixedTransits(
      int vehicle, double solve_duration_ratio,
      const std::function<int64_t(int64_t)>& next_accessor,
      const RoutingModel::RouteDimensionTravelInfo* dimension_travel_info,
      absl::Span<const int64_t> solution_cumul_values,
      absl::Span<const int64_t> solution_break_values, int64_t* solution_cost,
      int64_t* cost_offset = nullptr,
      bool reuse_previous_model_if_possible = false, bool clear_lp = true,
      absl::Duration* solve_duration = nullptr);

  // Similar to ComputeRouteCumuls, but also tries to pack the cumul values on
  // the route, such that the cost remains the same, the cumul of route end is
  // minimized, and then the cumul of the start of the route is maximized.
  // If 'resource' is non-null, the packed route must also respect its start/end
  // time window.
  DimensionSchedulingStatus ComputePackedRouteCumuls(
      int vehicle, double solve_duration_ratio,
      const std::function<int64_t(int64_t)>& next_accessor,
      const RoutingModel::RouteDimensionTravelInfo* dimension_travel_info,
      const RoutingModel::ResourceGroup::Resource* resource,
      std::vector<int64_t>* packed_cumuls, std::vector<int64_t>* packed_breaks);

  // TODO(user): Add a "resource" to the method.
  // TODO(user): Also pack the route at the end of the optimization.
  //                 --> Merge with the "packing" method ?
  DimensionSchedulingStatus ComputeRouteCumulsWithTransitTargets(
      int vehicle, double solve_duration_ratio,
      const std::function<int64_t(int64_t)>& next_accessor,
      absl::Span<const int64_t> transit_targets,
      DimensionCumulOptimizerCore::TransitTargetCost transit_target_cost,
      std::vector<int64_t>* optimal_transits,
      std::vector<int64_t>* optimal_cumuls,
      std::vector<int64_t>* optimal_breaks);

  const RoutingDimension* dimension() const {
    return optimizer_core_.dimension();
  }

 private:
  std::vector<std::unique_ptr<RoutingLinearSolverWrapper>> solver_;
  DimensionCumulOptimizerCore optimizer_core_;
};

class GlobalDimensionCumulOptimizer {
 public:
  GlobalDimensionCumulOptimizer(
      const RoutingDimension* dimension,
      RoutingSearchParameters::SchedulingSolver solver_type,
      RoutingSearchStats* search_stats);
  // If feasible, computes the optimal cost of the entire model with regards to
  // the optimizer_core_'s dimension costs, minimizing cumul soft lower/upper
  // bound costs and vehicle/global span costs, and stores it in "optimal_cost"
  // (if not null).
  // Returns true iff all the constraints can be respected.
  DimensionSchedulingStatus ComputeCumulCostWithoutFixedTransits(
      const std::function<int64_t(int64_t)>& next_accessor,
      int64_t* optimal_cost_without_transits);
  // If feasible, computes the optimal values for cumul, break and resource
  // variables, minimizing cumul soft lower/upper bound costs and vehicle/global
  // span costs, stores them in "optimal_cumuls" (if not null), "optimal_breaks"
  // and "optimal_resource_indices_per_group", and returns true.
  // Returns false if the routes are not feasible.
  DimensionSchedulingStatus ComputeCumuls(
      const std::function<int64_t(int64_t)>& next_accessor,
      const std::vector<RoutingModel::RouteDimensionTravelInfo>&
          dimension_travel_info_per_route,
      std::vector<int64_t>* optimal_cumuls,
      std::vector<int64_t>* optimal_breaks,
      std::vector<std::vector<int>>* optimal_resource_indices_per_group);

  // Similar to ComputeCumuls, but also tries to pack the cumul values on all
  // routes, such that the cost remains the same, the cumuls of route ends are
  // minimized, and then the cumuls of the starts of the routes are maximized.
  // NOTE: It's assumed that all resource variables (if any) are Bound() when
  // calling this method, so each vehicle's resource attributes are set as
  // constraint on its route and no resource assignment is required.
  DimensionSchedulingStatus ComputePackedCumuls(
      const std::function<int64_t(int64_t)>& next_accessor,
      const std::vector<RoutingModel::RouteDimensionTravelInfo>&
          dimension_travel_info_per_route,
      std::vector<int64_t>* packed_cumuls, std::vector<int64_t>* packed_breaks);

  const RoutingDimension* dimension() const {
    return optimizer_core_.dimension();
  }

 private:
  std::unique_ptr<RoutingLinearSolverWrapper> solver_;
  DimensionCumulOptimizerCore optimizer_core_;
};

// Finds the approximate (*) min-cost (i.e. best) assignment of all vehicles
// v ∈ 'vehicles' to resources, i.e. indices in [0..num_resources), where the
// costs of assigning a vehicle v to a resource r of class r_c is given by
// 'vehicle_to_resource_class_assignment_costs(v)[r_c]', unless the latter is
// empty in which case vehicle v does not need a resource.
//
// Returns the cost of that optimal assignment, or -1 if it's infeasible.
// Moreover, if 'resource_indices' != nullptr, it assumes that its size is the
// global number of vehicles, and assigns its element #v with the resource r
// assigned to v, or -1 if none.
//
// (*) COST SCALING: When the costs are so large that they could possibly yield
// int64_t overflow, this method returns a *lower* bound of the actual optimal
// cost, and the assignment output in 'resource_indices' may be suboptimal if
// that lower bound isn't tight (but it should be very close).
//
// COMPLEXITY: in practice, should be roughly
// O(num_resource_classes * vehicles.size() + resource_indices->size()).
int64_t ComputeBestVehicleToResourceAssignment(
    absl::Span<const int> vehicles,
    const util_intops::StrongVector<RoutingModel::ResourceClassIndex,
                                    std::vector<int>>&
        resource_indices_per_class,
    const util_intops::StrongVector<RoutingModel::ResourceClassIndex,
                                    absl::flat_hash_set<int>>&
        ignored_resources_per_class,
    std::function<const std::vector<int64_t>*(int)>
        vehicle_to_resource_class_assignment_costs,
    std::vector<int>* resource_indices);

// Computes the vehicle-to-resource-class assignment costs for the given vehicle
// to all resource classes in the group, and sets these costs in
// 'assignment_costs' (if non-null). The latter is cleared and kept empty if the
// vehicle 'v' should not have a resource assigned to it.
// optimize_vehicle_costs indicates if the costs should be optimized or if
// we merely care about feasibility (cost of 0) and infeasibility (cost of -1)
// of the assignments.
// The cumul and break values corresponding to the assignment of each resource
// are also set in cumul_values and break_values, if non-null.
bool ComputeVehicleToResourceClassAssignmentCosts(
    int v, double solve_duration_ratio,
    const RoutingModel::ResourceGroup& resource_group,
    const util_intops::StrongVector<RoutingModel::ResourceClassIndex,
                                    absl::flat_hash_set<int>>&
        ignored_resources_per_class,
    const std::function<int64_t(int64_t)>& next_accessor,
    const std::function<int64_t(int64_t, int64_t)>& transit_accessor,
    bool optimize_vehicle_costs, LocalDimensionCumulOptimizer* lp_optimizer,
    LocalDimensionCumulOptimizer* mp_optimizer,
    std::vector<int64_t>* assignment_costs,
    std::vector<std::vector<int64_t>>* cumul_values,
    std::vector<std::vector<int64_t>>* break_values);

// Structure to store the slope and y_intercept of a segment of a piecewise
// linear function.
struct SlopeAndYIntercept {
  double slope;
  double y_intercept;

  friend ::std::ostream& operator<<(::std::ostream& os,
                                    const SlopeAndYIntercept& it) {
    return os << "{" << it.slope << ", " << it.y_intercept << "}";
  }
};

// Given a FloatSlopePiecewiseLinearFunction, returns a vector of slope and
// y-intercept corresponding to each segment. Only the segments in
// [index_start, index_end[ will be considered.
// TODO(user): Consider making the following two functions methods of
// FloatSlopePiecewiseLinearFunction. They're only called in lp_scheduling.cc
// and ../tour_optimization/model_test.cc, but they might come in handy.
std::vector<SlopeAndYIntercept> PiecewiseLinearFunctionToSlopeAndYIntercept(
    const FloatSlopePiecewiseLinearFunction& pwl_function, int index_start = 0,
    int index_end = -1);

// Converts a vector of SlopeAndYIntercept to a vector of convexity regions.
// Convexity regions are defined such that, all segment in a convexity region
// form a convex function. The boolean in the vector is set to true if the
// segment associated to it starts a new convexity region. Therefore, a convex
// function would yield {true, false, false, ...} and a concave function would
// yield {true, true, true, ...}.
std::vector<bool> SlopeAndYInterceptToConvexityRegions(
    absl::Span<const SlopeAndYIntercept> slope_and_y_intercept);

}  // namespace operations_research

#endif  // ORTOOLS_CONSTRAINT_SOLVER_ROUTING_LP_SCHEDULING_H_