VehicleRouting

class VehicleRouting(graph, num_vehicles=2, depot=0)[source]

Bases: GraphOptimizationApplication

Optimization application for the “vehicle routing problem” [1] based on a NetworkX graph.

References

[1]: “Vehicle routing problem”, https://en.wikipedia.org/wiki/Vehicle_routing_problem

Parameters:

  • graph (nx.Graph | np.ndarray | list) – A graph representing a problem. It can be specified directly as a NetworkX graph, or as an array or list format suitable to build out a NetworkX graph.

  • num_vehicles (int) – The number of vehicles

  • depot (int) – The index of the depot node where all the vehicle depart

Attributes

depot

Getter of depot

Returns:

The node index of the depot where all the vehicles depart

graph

Getter of the graph

Returns:

A graph for a problem

num_vehicles

Getter of num_vehicles

Returns:

The number of the vehicles

Methods

static create_random_instance(n, low=0, high=100, seed=None, num_vehicle=2, depot=0)[source]

Create a random instance of the vehicle routing problem.

Parameters:

  • n (int) – the number of nodes.

  • low (int) – The minimum value for the coordinate of a node.

  • high (int) – The maximum value for the coordinate of a node.

  • seed (int | None) – the seed for the random coordinates.

  • num_vehicle (int) – The number of the vehicles

  • depot (int) – The index of the depot node where all the vehicle depart

Returns:

A VehicleRouting instance created from the input information

Return type:

VehicleRouting

draw(result=None, pos=None)

Draw a graph with the result. When the result is None, draw an original graph without colors.

Parameters:

  • result (OptimizationResult | np.ndarray | None) – The calculated result for the problem

  • pos (dict[int, np.ndarray] | None) – The positions of nodes

interpret(result)[source]

Interpret a result as a list of the routes for each vehicle

Parameters:

result (OptimizationResult | np.ndarray) – The calculated result of the problem

Returns:

A list of the routes for each vehicle

Return type:

list[list[list[int]]]

static sample_most_likely(state_vector)

Compute the most likely binary string from state vector.

Parameters:

state_vector (QuasiDistribution | Statevector | np.ndarray | dict) – state vector or counts or quasi-probabilities.

Returns:

binary string as numpy.ndarray of ints.

Raises:

ValueError – if state_vector is not QuasiDistribution, Statevector, np.ndarray, or dict.

Return type:

np.ndarray

to_quadratic_program()[source]

Convert a vehicle routing problem instance into a QuadraticProgram

Returns:

The QuadraticProgram created from the vehicle routing problem instance.

Return type:

QuadraticProgram