--- Path insertion heuristics --- Make all nodes inactive.

Lets the solver detect which strategy to use according to the model being solved.

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.

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).

Same as PATH_CHEAPEST_ARC, except that arc costs are evaluated using the function passed to RoutingModel::SetFirstSolutionEvaluator() (cf.

Select the first node with an unbound successor and connect it to the first available node.

--- Variable-based heuristics --- Iteratively connect two nodes which produce the cheapest route segment.

Select the first node with an unbound successor and connect it to the node which produces the cheapest route segment.

Iteratively build a solution by inserting each node at its cheapest position; the cost of insertion is based on the arc cost function.

Iteratively build a solution by inserting the cheapest node at its cheapest position; the cost of insertion is based on the arc cost function.

--- 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.

Same as PATH_CHEAPEST_ARC, but arcs are evaluated with a comparison-based selector which will favor the most constrained arc first.

Savings algorithm (Clarke & Wright).

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.

Sweep algorithm (Wren & Holliday).

See the homonymous value in LocalSearchMetaheuristic.

Lets the solver select the metaheuristic.

Uses tabu search on a list of variables to escape local minima.

Accepts improving (cost-reducing) local search neighbors until a local minimum is reached.

Uses guided local search to escape local minima (cf.

Uses simulated annealing to escape local minima (cf.

Uses tabu search to escape local minima (cf.

Means "not set".

Enum value maps for ConstraintSolverParameters_TrailCompression.

Enum value maps for ConstraintSolverParameters_TrailCompression.

Enum value maps for FirstSolutionStrategy_Value.

Enum value maps for FirstSolutionStrategy_Value.

Enum value maps for LocalSearchMetaheuristic_Value.

Enum value maps for LocalSearchMetaheuristic_Value.

Enum value maps for RoutingSearchParameters_SchedulingSolver.

Enum value maps for RoutingSearchParameters_SchedulingSolver.

Solver parameters.

First solution strategies, used as starting point of local search.

Local search metaheuristics used to guide the search.

A search limit The default values for int64 fields is the maxima value, i.e., 2^63-1.

Parameters which have to be set when creating a RoutingModel.

Parameters defining the search used to solve vehicle routing problems.

Local search neighborhood operators used to build a solutions neighborhood.

Internal parameters of the solver.

Underlying solver to use in dimension scheduling, respectively for continuous and mixed models.