// 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.

// Enums used to define routing parameters.

syntax = "proto3";

option java_package = "com.google.ortools.constraintsolver";
option java_multiple_files = true;
option csharp_namespace = "Google.OrTools.ConstraintSolver";

package operations_research;

// First solution strategies, used as starting point of local search.
message FirstSolutionStrategy {
  enum Value {
    // See the homonymous value in LocalSearchMetaheuristic.
    UNSET = 0;

    // Lets the solver detect which strategy to use according to the model being
    // solved.
    AUTOMATIC = 15;

    // --- Path addition heuristics ---
    // Starting from a route "start" node, connect it to the node which produces
    // the cheapest route segment, then extend the route by iterating on the
    // last node added to the route.
    PATH_CHEAPEST_ARC = 3;
    // Same as PATH_CHEAPEST_ARC, but arcs are evaluated with a comparison-based
    // selector which will favor the most constrained arc first. To assign a
    // selector to the routing model, see
    // RoutingModel::ArcIsMoreConstrainedThanArc() in routing.h for details.
    PATH_MOST_CONSTRAINED_ARC = 4;
    // Same as PATH_CHEAPEST_ARC, except that arc costs are evaluated using the
    // function passed to RoutingModel::SetFirstSolutionEvaluator()
    // (cf. routing.h).
    EVALUATOR_STRATEGY = 5;
    // Savings algorithm (Clarke & Wright).
    // Reference: Clarke, G. & Wright, J.W.:
    // "Scheduling of Vehicles from a Central Depot to a Number of Delivery
    // Points", Operations Research, Vol. 12, 1964, pp. 568-581
    SAVINGS = 10;
    // Parallel version of the Savings algorithm.
    // Instead of extending a single route until it is no longer possible,
    // the parallel version iteratively considers the next most improving
    // feasible saving and possibly builds several routes in parallel.
    PARALLEL_SAVINGS = 17;
    // Sweep algorithm (Wren & Holliday).
    // Reference: Anthony Wren & Alan Holliday: Computer Scheduling of Vehicles
    // from One or More Depots to a Number of Delivery Points Operational
    // Research Quarterly (1970-1977),
    // Vol. 23, No. 3 (Sep., 1972), pp. 333-344
    SWEEP = 11;
    // Christofides algorithm (actually a variant of the Christofides algorithm
    // using a maximal matching instead of a maximum matching, which does
    // not guarantee the 3/2 factor of the approximation on a metric travelling
    // salesman). Works on generic vehicle routing models by extending a route
    // until no nodes can be inserted on it.
    // Reference: Nicos Christofides, Worst-case analysis of a new heuristic for
    // the travelling salesman problem, Report 388, Graduate School of
    // Industrial Administration, CMU, 1976.
    CHRISTOFIDES = 13;

    // --- Path insertion heuristics ---
    // Make all nodes inactive. Only finds a solution if nodes are optional (are
    // element of a disjunction constraint with a finite penalty cost).
    ALL_UNPERFORMED = 6;
    // Iteratively build a solution by inserting the cheapest node at its
    // cheapest position; the cost of insertion is based on the global cost
    // function of the routing model. As of 2/2012, only works on models with
    // optional nodes (with finite penalty costs).
    BEST_INSERTION = 7;
    // Iteratively build a solution by inserting the cheapest node at its
    // cheapest position; the cost of insertion is based on the arc cost
    // function. Is faster than BEST_INSERTION.
    PARALLEL_CHEAPEST_INSERTION = 8;
    // Iteratively build a solution by constructing routes sequentially, for
    // each route inserting the cheapest node at its cheapest position until the
    // route is completed; the cost of insertion is based on the arc cost
    // function. Is faster than PARALLEL_CHEAPEST_INSERTION.
    SEQUENTIAL_CHEAPEST_INSERTION = 14;
    // Iteratively build a solution by inserting each node at its cheapest
    // position; the cost of insertion is based on the arc cost function.
    // Differs from PARALLEL_CHEAPEST_INSERTION by the node selected for
    // insertion; here nodes are considered in decreasing order of distance to
    // the start/ends of the routes, i.e. farthest nodes are inserted first.
    // Is faster than SEQUENTIAL_CHEAPEST_INSERTION.
    LOCAL_CHEAPEST_INSERTION = 9;
    // Same as LOCAL_CHEAPEST_INSERTION except that the cost of insertion is
    // based on the routing model cost function instead of arc costs only.
    LOCAL_CHEAPEST_COST_INSERTION = 16;

    // --- Variable-based heuristics ---
    // Iteratively connect two nodes which produce the cheapest route segment.
    GLOBAL_CHEAPEST_ARC = 1;
    // Select the first node with an unbound successor and connect it to the
    // node which produces the cheapest route segment.
    LOCAL_CHEAPEST_ARC = 2;
    // Select the first node with an unbound successor and connect it to the
    // first available node.
    // This is equivalent to the CHOOSE_FIRST_UNBOUND strategy combined with
    // ASSIGN_MIN_VALUE (cf. constraint_solver.h).
    FIRST_UNBOUND_MIN_VALUE = 12;
  }
}

// Local search metaheuristics used to guide the search. Apart from greedy
// descent, they will try to escape local minima.
message LocalSearchMetaheuristic {
  enum Value {
    // Means "not set". If the solver sees that, it'll behave like for
    // AUTOMATIC. But this value won't override others upon a proto MergeFrom(),
    // whereas "AUTOMATIC" will.
    UNSET = 0;

    // Lets the solver select the metaheuristic.
    AUTOMATIC = 6;

    // Accepts improving (cost-reducing) local search neighbors until a local
    // minimum is reached.
    GREEDY_DESCENT = 1;
    // Uses guided local search to escape local minima
    // (cf. http://en.wikipedia.org/wiki/Guided_Local_Search); this is generally
    // the most efficient metaheuristic for vehicle routing.
    GUIDED_LOCAL_SEARCH = 2;
    // Uses simulated annealing to escape local minima
    // (cf. http://en.wikipedia.org/wiki/Simulated_annealing).
    SIMULATED_ANNEALING = 3;
    // Uses tabu search to escape local minima
    // (cf. http://en.wikipedia.org/wiki/Tabu_search).
    TABU_SEARCH = 4;
    // Uses tabu search on a list of variables to escape local minima. The list
    // of variables to use must be provided via the SetTabuVarsCallback
    // callback.
    GENERIC_TABU_SEARCH = 5;
  }
}

// Used by `RoutingModel` to report the status of the search for a solution.
message RoutingSearchStatus {
  enum Value {
    // Problem not solved yet (before calling RoutingModel::Solve()).
    ROUTING_NOT_SOLVED = 0;
    // Problem solved successfully after calling RoutingModel::Solve().
    ROUTING_SUCCESS = 1;
    // Problem solved successfully after calling RoutingModel::Solve(), except
    // that a local optimum has not been reached. Leaving more time would allow
    // improving the solution.
    ROUTING_PARTIAL_SUCCESS_LOCAL_OPTIMUM_NOT_REACHED = 2;
    // No solution found to the problem after calling RoutingModel::Solve().
    ROUTING_FAIL = 3;
    // Time limit reached before finding a solution with RoutingModel::Solve().
    ROUTING_FAIL_TIMEOUT = 4;
    // Model, model parameters or flags are not valid.
    ROUTING_INVALID = 5;
    // Problem proven to be infeasible.
    ROUTING_INFEASIBLE = 6;
    // Problem has been solved to optimality.
    ROUTING_OPTIMAL = 7;
  }
}