routing_parameters.proto

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// Copyright 2010-2024 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.
 
// Protocol buffer used to parametrize the routing library, in particular the
// search parameters such as first solution heuristics and local search
// neighborhoods.
 
syntax = "proto3";
 
option java_package = "com.google.ortools.constraintsolver";
option java_multiple_files = true;
option csharp_namespace = "Google.OrTools.ConstraintSolver";
 
import "google/protobuf/duration.proto";
import "ortools/constraint_solver/routing_enums.proto";
import "ortools/constraint_solver/routing_ils.proto";
import "ortools/constraint_solver/solver_parameters.proto";
import "ortools/sat/sat_parameters.proto";
import "ortools/util/optional_boolean.proto";
 
package operations_research;
 
// Parameters defining the search used to solve vehicle routing problems.
//
// If a parameter is unset (or, equivalently, set to its default value),
// then the routing library will pick its preferred value for that parameter
// automatically: this should be the case for most parameters.
// To see those "default" parameters, call GetDefaultRoutingSearchParameters().
// Next ID: 61
message RoutingSearchParameters {
  // First solution strategies, used as starting point of local search.
  FirstSolutionStrategy.Value first_solution_strategy = 1;
 
  // --- Advanced first solutions strategy settings ---
  // Don't touch these unless you know what you are doing.
  //
  // Use filtered version of first solution strategy if available.
  bool use_unfiltered_first_solution_strategy = 2;
  // Parameters specific to the Savings first solution heuristic.
  // Ratio (in ]0, 1]) of neighbors to consider for each node when constructing
  // the savings. If unspecified, its value is considered to be 1.0.
  double savings_neighbors_ratio = 14;
  // The number of neighbors considered for each node in the Savings heuristic
  // is chosen so that the space used to store the savings doesn't exceed
  // savings_max_memory_usage_bytes, which must be in ]0, 1e10].
  // NOTE: If both savings_neighbors_ratio and savings_max_memory_usage_bytes
  // are specified, the number of neighbors considered for each node will be the
  // minimum of the two numbers determined by these parameters.
  double savings_max_memory_usage_bytes = 23;
  // Add savings related to reverse arcs when finding the nearest neighbors
  // of the nodes.
  bool savings_add_reverse_arcs = 15;
  // Coefficient of the cost of the arc for which the saving value is being
  // computed:
  // Saving(a-->b) = Cost(a-->end) + Cost(start-->b)
  //                 - savings_arc_coefficient * Cost(a-->b)
  // This parameter must be greater than 0, and its default value is 1.
  double savings_arc_coefficient = 18;
  // When true, the routes are built in parallel, sequentially otherwise.
  bool savings_parallel_routes = 19;
 
  // Ratio (between 0 and 1) of available vehicles in the model on which
  // farthest nodes of the model are inserted as seeds in the
  // GlobalCheapestInsertion first solution heuristic.
  double cheapest_insertion_farthest_seeds_ratio = 16;
 
  // Ratio (in ]0, 1]) of closest non start/end nodes to consider as neighbors
  // for each node when creating new insertions in the parallel/sequential
  // cheapest insertion heuristic.
  // If not overridden, its default value is 1, meaning all neighbors will be
  // considered.
  // The neighborhood ratio is coupled with the corresponding min_neighbors
  // integer, indicating the minimum number of neighbors to consider for each
  // node:
  // num_closest_neighbors =
  //        max(min_neighbors, neighbors_ratio * NUM_NON_START_END_NODES)
  // This minimum number of neighbors must be greater or equal to 1, its
  // default value.
  //
  // Neighbors ratio and minimum number of neighbors for the first solution
  // heuristic.
  double cheapest_insertion_first_solution_neighbors_ratio = 21;
  int32 cheapest_insertion_first_solution_min_neighbors = 44;
  // Neighbors ratio and minimum number of neighbors for the heuristic when used
  // in a local search operator (see
  // local_search_operators.use_global_cheapest_insertion_path_lns and
  // local_search_operators.use_global_cheapest_insertion_chain_lns below).
  double cheapest_insertion_ls_operator_neighbors_ratio = 31;
  int32 cheapest_insertion_ls_operator_min_neighbors = 45;
 
  // Whether or not to only consider closest neighbors when initializing the
  // assignment for the first solution.
  bool
      cheapest_insertion_first_solution_use_neighbors_ratio_for_initialization =
          46;
  // Whether or not to consider entries making the nodes/pairs unperformed in
  // the GlobalCheapestInsertion heuristic.
  bool cheapest_insertion_add_unperformed_entries = 40;
 
  // In insertion-based heuristics, describes what positions must be considered
  // when inserting a pickup/delivery pair, and in what order they are
  // considered.
  enum PairInsertionStrategy {
    // Let the solver decide the set of positions and its ordering.
    AUTOMATIC = 0;
    // Consider all positions, by increasing (cost(pickup), cost(delivery)).
    BEST_PICKUP_THEN_BEST_DELIVERY = 1;
    // Consider all positions, by increasing by cost(pickup) + cost(delivery).
    BEST_PICKUP_DELIVERY_PAIR = 2;
    // Only consider insertion positions that are compatible with the multitour
    // property, meaning a series of pickups may only start when the vehicle
    // is not carrying any delivery. This setting is designed to explore much
    // less possibilities than the full BEST_PICKUP_DELIVERY_PAIR.
    // Order by increasing by cost(pickup) + cost(delivery).
    BEST_PICKUP_DELIVERY_PAIR_MULTITOUR = 3;
  }
  // Choice of insertion strategy for pickup/delivery pairs, used in local
  // cheapest insertion, both first solution heuristic and LNS.
  PairInsertionStrategy local_cheapest_insertion_pickup_delivery_strategy = 49;
  // Choice of insertion strategy for pickup/delivery pairs, used in local
  // cheapest cost insertion, both first solution heuristic and LNS.
  PairInsertionStrategy local_cheapest_cost_insertion_pickup_delivery_strategy =
      55;
 
  // If true use minimum matching instead of minimal matching in the
  // Christofides algorithm.
  bool christofides_use_minimum_matching = 30;
 
  // If non zero, a period p indicates that every p node insertions or additions
  // to a path, an optimization of the current partial solution will be
  // performed. As of 12/2023:
  // - this requires that a secondary routing model has been passed to the main
  //   one,
  // - this is only supported by LOCAL_CHEAPEST_INSERTION and
  // LOCAL_CHEAPEST_COST_INSERTION.
  int32 first_solution_optimization_period = 59;
 
  // Local search neighborhood operators used to build a solutions neighborhood.
  // Next ID: 35
  message LocalSearchNeighborhoodOperators {
    // --- Inter-route operators ---
    // Operator which moves a single node to another position.
    // Possible neighbors for the path 1 -> 2 -> 3 -> 4 -> 5
    // (where (1, 5) are first and last nodes of the path and can therefore not
    // be moved):
    //   1 ->  3  -> [2] ->  4  -> 5
    //   1 ->  3  ->  4  -> [2] -> 5
    //   1 ->  2  ->  4  -> [3] -> 5
    //   1 -> [4] ->  2  ->  3  -> 5
    OptionalBoolean use_relocate = 1;
    // Operator which moves a pair of pickup and delivery nodes to another
    // position where the first node of the pair must be before the second node
    // on the same path. Compared to the light_relocate_pair operator, tries all
    // possible positions of insertion of a pair (not only after another pair).
    // Possible neighbors for the path 1 -> A -> B -> 2 -> 3 (where (1, 3) are
    // first and last nodes of the path and can therefore not be moved, and
    // (A, B) is a pair of nodes):
    //   1 -> [A] ->  2  -> [B] -> 3
    //   1 ->  2  -> [A] -> [B] -> 3
    OptionalBoolean use_relocate_pair = 2;
    // Operator which moves a pair of pickup and delivery nodes after another
    // pair.
    // Possible neighbors for paths 1 -> A -> B -> 2, 3 -> C -> D -> 4 (where
    // (1, 2) and (3, 4) are first and last nodes of paths and can therefore not
    // be moved, and (A, B) and (C, D) are pair of nodes):
    //   1 -> 2, 3 -> C -> [A] -> D -> [B] -> 4
    //   1 -> A -> [C] -> B -> [D] -> 2, 3 -> 4
    OptionalBoolean use_light_relocate_pair = 24;
    // Relocate neighborhood which moves chains of neighbors.
    // The operator starts by relocating a node n after a node m, then continues
    // moving nodes which were after n as long as the "cost" added is less than
    // the "cost" of the arc (m, n). If the new chain doesn't respect the domain
    // of next variables, it will try reordering the nodes until it finds a
    // valid path.
    // Possible neighbors for path 1 -> A -> B -> C -> D -> E -> 2 (where (1, 2)
    // are first and last nodes of the path and can therefore not be moved, A
    // must be performed before B, and A, D and E are located at the same
    // place):
    // 1 -> A -> C -> [B] -> D -> E -> 2
    // 1 -> A -> C -> D -> [B] -> E -> 2
    // 1 -> A -> C -> D -> E -> [B] -> 2
    // 1 -> A -> B -> D -> [C] -> E -> 2
    // 1 -> A -> B -> D -> E -> [C] -> 2
    // 1 -> A -> [D] -> [E] -> B -> C -> 2
    // 1 -> A -> B -> [D] -> [E] ->  C -> 2
    // 1 -> A -> [E] -> B -> C -> D -> 2
    // 1 -> A -> B -> [E] -> C -> D -> 2
    // 1 -> A -> B -> C -> [E] -> D -> 2
    // This operator is extremely useful to move chains of nodes which are
    // located at the same place (for instance nodes part of a same stop).
    OptionalBoolean use_relocate_neighbors = 3;
    // Relocate neighborhood that moves subpaths all pickup and delivery
    // pairs have both pickup and delivery inside the subpath or both outside
    // the subpath. For instance, for given paths:
    // 0 -> A -> B -> A' -> B' -> 5 -> 6 -> 8
    // 7 -> 9
    // Pairs (A,A') and (B,B') are interleaved, so the expected neighbors are:
    // 0 -> 5 -> A -> B -> A' -> B' -> 6 -> 8
    // 7 -> 9
    //
    // 0 -> 5 -> 6 -> A -> B -> A' -> B' -> 8
    // 7 -> 9
    //
    // 0 -> 5 -> 6 -> 8
    // 7 -> A -> B -> A' -> B' -> 9
    OptionalBoolean use_relocate_subtrip = 25;
    // Operator which exchanges the positions of two nodes.
    // Possible neighbors for the path 1 -> 2 -> 3 -> 4 -> 5
    // (where (1, 5) are first and last nodes of the path and can therefore not
    // be moved):
    //   1 -> [3] -> [2] ->  4  -> 5
    //   1 -> [4] ->  3  -> [2] -> 5
    //   1 ->  2  -> [4] -> [3] -> 5
    OptionalBoolean use_exchange = 4;
    // Operator which exchanges the positions of two pair of nodes. Pairs
    // correspond to the pickup and delivery pairs defined in the routing model.
    // Possible neighbor for the paths
    // 1 -> A -> B -> 2 -> 3 and 4 -> C -> D -> 5
    // (where (1, 3) and (4, 5) are first and last nodes of the paths and can
    // therefore not be moved, and (A, B) and (C,D) are pairs of nodes):
    //   1 -> [C] ->  [D] -> 2 -> 3, 4 -> [A] -> [B] -> 5
    OptionalBoolean use_exchange_pair = 22;
    // Operator which exchanges subtrips associated to two pairs of nodes,
    // see use_relocate_subtrip for a definition of subtrips.
    OptionalBoolean use_exchange_subtrip = 26;
    // Operator which cross exchanges the starting chains of 2 paths, including
    // exchanging the whole paths.
    // First and last nodes are not moved.
    // Possible neighbors for the paths 1 -> 2 -> 3 -> 4 -> 5 and 6 -> 7 -> 8
    // (where (1, 5) and (6, 8) are first and last nodes of the paths and can
    // therefore not be moved):
    //   1 -> [7] -> 3 -> 4 -> 5  6 -> [2] -> 8
    //   1 -> [7] -> 4 -> 5       6 -> [2 -> 3] -> 8
    //   1 -> [7] -> 5            6 -> [2 -> 3 -> 4] -> 8
    OptionalBoolean use_cross = 5;
    // Not implemented yet. TODO(b/68128619): Implement.
    OptionalBoolean use_cross_exchange = 6;
    // Operator which detects the relocate_expensive_chain_num_arcs_to_consider
    // most expensive arcs on a path, and moves the chain resulting from cutting
    // pairs of arcs among these to another position.
    // Possible neighbors for paths 1 -> 2 (empty) and
    // 3 -> A ------> B --> C -----> D -> 4 (where A -> B and C -> D are the 2
    // most expensive arcs, and the chain resulting from breaking them is
    // B -> C):
    //   1 -> [B -> C] -> 2     3 -> A -> D -> 4
    //   1 -> 2      3 -> [B -> C] -> A -> D -> 4
    //   1 -> 2      3 -> A -> D -> [B -> C] -> 4
    OptionalBoolean use_relocate_expensive_chain = 23;
    // --- Intra-route operators ---
    // Operator which reverses a subchain of a path. It is called TwoOpt
    // because it breaks two arcs on the path; resulting paths are called
    // two-optimal.
    // Possible neighbors for the path 1 -> 2 -> 3 -> 4 -> 5
    // (where (1, 5) are first and last nodes of the path and can therefore not
    // be moved):
    //   1 -> [3 -> 2] -> 4  -> 5
    //   1 -> [4 -> 3  -> 2] -> 5
    //   1 ->  2 -> [4 -> 3] -> 5
    OptionalBoolean use_two_opt = 7;
    // Operator which moves sub-chains of a path of length 1, 2 and 3 to another
    // position in the same path.
    // When the length of the sub-chain is 1, the operator simply moves a node
    // to another position.
    // Possible neighbors for the path 1 -> 2 -> 3 -> 4 -> 5, for a sub-chain
    // length of 2 (where (1, 5) are first and last nodes of the path and can
    // therefore not be moved):
    //   1 ->  4 -> [2 -> 3] -> 5
    //   1 -> [3 -> 4] -> 2  -> 5
    // The OR_OPT operator is a limited version of 3-Opt (breaks 3 arcs on a
    // path).
    OptionalBoolean use_or_opt = 8;
    // Lin-Kernighan operator.
    // While the accumulated local gain is positive, performs a 2-OPT or a 3-OPT
    // move followed by a series of 2-OPT moves. Returns a neighbor for which
    // the global gain is positive.
    OptionalBoolean use_lin_kernighan = 9;
    // Sliding TSP operator.
    // Uses an exact dynamic programming algorithm to solve the TSP
    // corresponding to path sub-chains.
    // For a subchain 1 -> 2 -> 3 -> 4 -> 5 -> 6, solves the TSP on
    // nodes A, 2, 3, 4, 5, where A is a merger of nodes 1 and 6 such that
    // cost(A,i) = cost(1,i) and cost(i,A) = cost(i,6).
    OptionalBoolean use_tsp_opt = 10;
    // --- Operators on inactive nodes ---
    // Operator which inserts an inactive node into a path.
    // Possible neighbors for the path 1 -> 2 -> 3 -> 4 with 5 inactive
    // (where 1 and 4 are first and last nodes of the path) are:
    //   1 -> [5] ->  2  ->  3  -> 4
    //   1 ->  2  -> [5] ->  3  -> 4
    //   1 ->  2  ->  3  -> [5] -> 4
    OptionalBoolean use_make_active = 11;
    // Operator which relocates a node while making an inactive one active.
    // As of 3/2017, the operator is limited to two kinds of moves:
    // - Relocating a node and replacing it by an inactive node.
    //   Possible neighbor for path 1 -> 5, 2 -> 3 -> 6 and 4 inactive
    //   (where 1,2 and 5,6 are first and last nodes of paths) is:
    //   1 -> 3 -> 5, 2 -> 4 -> 6.
    // - Relocating a node and inserting an inactive node next to it.
    //   Possible neighbor for path 1 -> 5, 2 -> 3 -> 6 and 4 inactive
    //   (where 1,2 and 5,6 are first and last nodes of paths) is:
    //   1 -> 4 -> 3 -> 5, 2 -> 6.
    OptionalBoolean use_relocate_and_make_active = 21;
    // Operator which makes path nodes inactive.
    // Possible neighbors for the path 1 -> 2 -> 3 -> 4 (where 1 and 4 are first
    // and last nodes of the path) are:
    //   1 -> 3 -> 4 with 2 inactive
    //   1 -> 2 -> 4 with 3 inactive
    OptionalBoolean use_make_inactive = 12;
    // Operator which makes a "chain" of path nodes inactive.
    // Possible neighbors for the path 1 -> 2 -> 3 -> 4 (where 1 and 4 are first
    // and last nodes of the path) are:
    //   1 -> 3 -> 4 with 2 inactive
    //   1 -> 2 -> 4 with 3 inactive
    //   1 -> 4 with 2 and 3 inactive
    OptionalBoolean use_make_chain_inactive = 13;
    // Operator which replaces an active node by an inactive one.
    // Possible neighbors for the path 1 -> 2 -> 3 -> 4 with 5 inactive
    // (where 1 and 4 are first and last nodes of the path) are:
    //   1 -> [5] ->  3  -> 4 with 2 inactive
    //   1 ->  2  -> [5] -> 4 with 3 inactive
    OptionalBoolean use_swap_active = 14;
    // Operator which makes an inactive node active and an active one inactive.
    // It is similar to SwapActiveOperator excepts that it tries to insert the
    // inactive node in all possible positions instead of just the position of
    // the node made inactive.
    // Possible neighbors for the path 1 -> 2 -> 3 -> 4 with 5 inactive
    // (where 1 and 4 are first and last nodes of the path) are:
    //   1 -> [5] ->  3  -> 4 with 2 inactive
    //   1 ->  3  -> [5] -> 4 with 2 inactive
    //   1 -> [5] ->  2  -> 4 with 3 inactive
    //   1 ->  2  -> [5] -> 4 with 3 inactive
    OptionalBoolean use_extended_swap_active = 15;
    // Swaps active nodes from node alternatives in sequence. Considers chains
    // of nodes with alternatives, builds a DAG from the chain, each "layer" of
    // the DAG being composed of the set of alternatives of the node at a given
    // rank in the chain, fully connected to the next layer. A neighbor is built
    // from the shortest path starting from the node before the chain (source),
    // through the DAG to the node following the chain.
    OptionalBoolean use_shortest_path_swap_active = 34;
    // Operator which makes an inactive node active and an active pair of nodes
    // inactive OR makes an inactive pair of nodes active and an active node
    // inactive.
    // Possible neighbors for the path 1 -> 2 -> 3 -> 4 with 5 inactive
    // (where 1 and 4 are first and last nodes of the path and (2,3) is a pair
    // of nodes) are:
    //   1 -> [5] -> 4 with (2,3) inactive
    // Possible neighbors for the path 1 -> 2 -> 3 with (4,5) inactive
    // (where 1 and 3 are first and last nodes of the path and (4,5) is a pair
    // of nodes) are:
    //   1 -> [4] -> [5] -> 3 with 2 inactive
    OptionalBoolean use_node_pair_swap_active = 20;
    // --- Large neighborhood search operators ---
    // Operator which relaxes two sub-chains of three consecutive arcs each.
    // Each sub-chain is defined by a start node and the next three arcs. Those
    // six arcs are relaxed to build a new neighbor.
    // PATH_LNS explores all possible pairs of starting nodes and so defines
    // n^2 neighbors, n being the number of nodes.
    // Note that the two sub-chains can be part of the same path; they even may
    // overlap.
    OptionalBoolean use_path_lns = 16;
    // Operator which relaxes one entire path and all inactive nodes.
    OptionalBoolean use_full_path_lns = 17;
    // TSP-base LNS.
    // Randomly merges consecutive nodes until n "meta"-nodes remain and solves
    // the corresponding TSP.
    // This defines an "unlimited" neighborhood which must be stopped by search
    // limits. To force diversification, the operator iteratively forces each
    // node to serve as base of a meta-node.
    OptionalBoolean use_tsp_lns = 18;
    // Operator which relaxes all inactive nodes and one sub-chain of six
    // consecutive arcs. That way the path can be improved by inserting inactive
    // nodes or swapping arcs.
    OptionalBoolean use_inactive_lns = 19;
    // --- LNS-like large neighborhood search operators using heuristics ---
    // Operator which makes all nodes on a route unperformed, and reinserts them
    // using the GlobalCheapestInsertion heuristic.
    OptionalBoolean use_global_cheapest_insertion_path_lns = 27;
    // Same as above but using LocalCheapestInsertion as a heuristic.
    OptionalBoolean use_local_cheapest_insertion_path_lns = 28;
    // The following operator relocates an entire route to an empty path and
    // then tries to insert the unperformed nodes using the global cheapest
    // insertion heuristic.
    OptionalBoolean
        use_relocate_path_global_cheapest_insertion_insert_unperformed = 33;
    // This operator finds heuristic_expensive_chain_lns_num_arcs_to_consider
    // most expensive arcs on a route, makes the nodes in between pairs of these
    // expensive arcs unperformed, and reinserts them using the
    // GlobalCheapestInsertion heuristic.
    OptionalBoolean use_global_cheapest_insertion_expensive_chain_lns = 29;
    // Same as above but using LocalCheapestInsertion as a heuristic for
    // insertion.
    OptionalBoolean use_local_cheapest_insertion_expensive_chain_lns = 30;
    // The following operator makes a node and its
    // heuristic_close_nodes_lns_num_nodes closest neighbors unperformed along
    // with each of their corresponding performed pickup/delivery pairs, and
    // then reinserts them using the GlobalCheapestInsertion heuristic.
    OptionalBoolean use_global_cheapest_insertion_close_nodes_lns = 31;
    // Same as above, but insertion positions for nodes are determined by the
    // LocalCheapestInsertion heuristic.
    OptionalBoolean use_local_cheapest_insertion_close_nodes_lns = 32;
  }
  LocalSearchNeighborhoodOperators local_search_operators = 3;
 
  // Neighbors ratio and minimum number of neighbors considered in local
  // search operators (see cheapest_insertion_first_solution_neighbors_ratio
  // and cheapest_insertion_first_solution_min_neighbors for more information).
  double ls_operator_neighbors_ratio = 53;
  int32 ls_operator_min_neighbors = 54;
 
  // If true, the solver will use multi-armed bandit concatenate operators. It
  // dynamically chooses the next neighbor operator in order to get the best
  // objective improvement.
  bool use_multi_armed_bandit_concatenate_operators = 41;
 
  // Memory coefficient related to the multi-armed bandit compound operator.
  // Sets how much the objective improvement of previous accepted neighbors
  // influence the current average improvement.
  // This parameter should be between 0 and 1.
  double multi_armed_bandit_compound_operator_memory_coefficient = 42;
 
  // Positive parameter defining the exploration coefficient of the multi-armed
  // bandit compound operator. Sets how often we explore rarely used and
  // unsuccessful in the past operators
  double multi_armed_bandit_compound_operator_exploration_coefficient = 43;
 
  // Number of expensive arcs to consider cutting in the RelocateExpensiveChain
  // neighborhood operator (see
  // LocalSearchNeighborhoodOperators.use_relocate_expensive_chain()).
  // This parameter must be greater than 2.
  // NOTE(user): The number of neighbors generated by the operator for
  // relocate_expensive_chain_num_arcs_to_consider = K is around
  // K*(K-1)/2 * number_of_routes * number_of_nodes.
  int32 relocate_expensive_chain_num_arcs_to_consider = 20;
 
  // Number of expensive arcs to consider cutting in the
  // FilteredHeuristicExpensiveChainLNSOperator operator.
  int32 heuristic_expensive_chain_lns_num_arcs_to_consider = 32;
 
  // Number of closest nodes to consider for each node during the destruction
  // phase of the FilteredHeuristicCloseNodesLNSOperator.
  int32 heuristic_close_nodes_lns_num_nodes = 35;
 
  // Local search metaheuristics used to guide the search.
  LocalSearchMetaheuristic.Value local_search_metaheuristic = 4;
  // These are advanced settings which should not be modified unless you know
  // what you are doing.
  // Lambda coefficient used to penalize arc costs when GUIDED_LOCAL_SEARCH is
  // used. Must be positive.
  double guided_local_search_lambda_coefficient = 5;
  // Whether to reset penalties when a new best solution is found. The effect is
  // that a greedy descent is started before the next penalization phase.
  bool guided_local_search_reset_penalties_on_new_best_solution = 51;
 
  // --- Search control ---
  //
  // If true, the solver should use depth-first search rather than local search
  // to solve the problem.
  bool use_depth_first_search = 6;
  // If true, use the CP solver to find a solution. Either local or depth-first
  // search will be used depending on the value of use_depth_first_search. Will
  // be run before the CP-SAT solver (cf. use_cp_sat).
  OptionalBoolean use_cp = 28;
  // If true, use the CP-SAT solver to find a solution. If use_cp is also true,
  // the CP-SAT solver will be run after the CP solver if there is time
  // remaining and will use the CP solution as a hint for the CP-SAT search.
  // As of 5/2019, only TSP models can be solved.
  OptionalBoolean use_cp_sat = 27;
  // If true, use the CP-SAT solver to find a solution on generalized routing
  // model. If use_cp is also true, the CP-SAT solver will be run after the CP
  // solver if there is time remaining and will use the CP solution as a hint
  // for the CP-SAT search.
  OptionalBoolean use_generalized_cp_sat = 47;
  // If use_cp_sat or use_generalized_cp_sat is true, contains the SAT algorithm
  // parameters which will be used.
  sat.SatParameters sat_parameters = 48;
  // If use_cp_sat or use_generalized_cp_sat is true, will report intermediate
  // solutions found by CP-SAT to solution listeners.
  bool report_intermediate_cp_sat_solutions = 56;
  // If model.Size() is less than the threshold and that no solution has been
  // found, attempt a pass with CP-SAT.
  int32 fallback_to_cp_sat_size_threshold = 52;
  // Underlying solver to use in dimension scheduling, respectively for
  // continuous and mixed models.
  enum SchedulingSolver {
    SCHEDULING_UNSET = 0;
    SCHEDULING_GLOP = 1;
    SCHEDULING_CP_SAT = 2;
  }
  SchedulingSolver continuous_scheduling_solver = 33;
  SchedulingSolver mixed_integer_scheduling_solver = 34;
  // Setting this to true completely disables the LP and MIP scheduling in the
  // solver. This overrides the 2 SchedulingSolver options above.
  optional bool disable_scheduling_beware_this_may_degrade_performance = 50;
  // Minimum step by which the solution must be improved in local search. 0
  // means "unspecified". If this value is fractional, it will get rounded to
  // the nearest integer.
  double optimization_step = 7;
  // Number of solutions to collect during the search. Corresponds to the best
  // solutions found during the search. 0 means "unspecified".
  int32 number_of_solutions_to_collect = 17;
  // -- Search limits --
  // Limit to the number of solutions generated during the search. 0 means
  // "unspecified".
  int64 solution_limit = 8;
  // Limit to the time spent in the search.
  google.protobuf.Duration time_limit = 9;
  // Limit to the time spent in the completion search for each local search
  // neighbor.
  google.protobuf.Duration lns_time_limit = 10;
  // Ratio of the overall time limit spent in a secondary LS phase with only
  // intra-route and insertion operators, meant to "cleanup" the current
  // solution before stopping the search.
  // TODO(user): Since these operators are very fast, add a parameter to cap
  // the max time allocated for this second phase (e.g.
  // Duration max_secondary_ls_time_limit).
  double secondary_ls_time_limit_ratio = 57;
 
  // Parameters required for the improvement search limit.
  message ImprovementSearchLimitParameters {
    // Parameter that regulates exchange rate between objective improvement and
    // number of neighbors spent. The smaller the value, the sooner the limit
    // stops the search. Must be positive.
    double improvement_rate_coefficient = 38;
    // Parameter that specifies the distance between improvements taken into
    // consideration for calculating the improvement rate.
    // Example: For 5 objective improvements = (10, 8, 6, 4, 2), and the
    // solutions_distance parameter of 2, then the improvement_rate will be
    // computed for (10, 6), (8, 4), and (6, 2).
    int32 improvement_rate_solutions_distance = 39;
  }
  // The improvement search limit is added to the solver if the following
  // parameters are set.
  ImprovementSearchLimitParameters improvement_limit_parameters = 37;
 
  // --- Propagation control ---
  // These are advanced settings which should not be modified unless you know
  // what you are doing.
  //
  // Use constraints with full propagation in routing model (instead of 'light'
  // propagation only). Full propagation is only necessary when using
  // depth-first search or for models which require strong propagation to
  // finalize the value of secondary variables.
  // Changing this setting to true will slow down the search in most cases and
  // increase memory consumption in all cases.
  bool use_full_propagation = 11;
 
  // --- Miscellaneous ---
  // Some of these are advanced settings which should not be modified unless you
  // know what you are doing.
  //
  // Activates search logging. For each solution found during the search, the
  // following will be displayed: its objective value, the maximum objective
  // value since the beginning of the search, the elapsed time since the
  // beginning of the search, the number of branches explored in the search
  // tree, the number of failures in the search tree, the depth of the search
  // tree, the number of local search neighbors explored, the number of local
  // search neighbors filtered by local search filters, the number of local
  // search neighbors accepted, the total memory used and the percentage of the
  // search done.
  bool log_search = 13;
  // In logs, cost values will be scaled and offset by the given values in the
  // following way: log_cost_scaling_factor * (cost + log_cost_offset)
  double log_cost_scaling_factor = 22;
  double log_cost_offset = 29;
  // In logs, this tag will be appended to each line corresponding to a new
  // solution. Useful to sort out logs when several solves are run in parallel.
  string log_tag = 36;
 
  // Whether the solver should use an Iterated Local Search approach to solve
  // the problem.
  bool use_iterated_local_search = 58;
 
  // Iterated Local Search parameters.
  IteratedLocalSearchParameters iterated_local_search_parameters = 60;
}
 
// Parameters which have to be set when creating a RoutingModel.
message RoutingModelParameters {
  // Parameters to use in the underlying constraint solver.
  ConstraintSolverParameters solver_parameters = 1;
  // Advanced settings.
  // If set to true reduction of the underlying constraint model will be
  // attempted when all vehicles have exactly the same cost structure. This can
  // result in significant speedups.
  bool reduce_vehicle_cost_model = 2;
  // Cache callback calls if the number of nodes in the model is less or equal
  // to this value.
  int32 max_callback_cache_size = 3;
}