qiskit_addon_opt_mapper.applications.VehicleRouting

class VehicleRouting(graph, num_vehicles=2, depot=0)

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

Init method.

Parameters:

• graph (Graph | 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

__init__(graph, num_vehicles=2, depot=0)

Init method.

Parameters:

• graph (Graph | 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

Return type:

None

Methods

__init__(graph[, num_vehicles, depot])	Init method.
create_random_instance(n[, low, high, seed, ...])	Create a random instance of the vehicle routing problem.
draw([result, pos])	Draw a graph with the result.
interpret(result)	Interpret a result as a list of the routes for each vehicle.
sample_most_likely(state_vector)	Compute the most likely binary string from state vector.
to_optimization_problem()	Represent as an optimization problem.

Attributes

depot	Getter of depot.
graph	Getter of the graph.
num_vehicles	Getter of num_vehicles.
nx_graph	Getter of the graph in Networkx format.

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

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

property depot: int

Getter of depot.

Returns:

The node index of the depot where all the vehicles depart

draw(result=None, pos=None)

Draw a graph with the result.

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

Parameters:

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

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

Return type:

None

property graph: PyGraph

Getter of the graph.

Returns:

A graph for a problem

interpret(result)

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

Parameters:

result (ndarray) – The calculated result of the problem

Returns:

A list of the routes for each vehicle

Return type:

list[list[list[int]]]

property num_vehicles: int

Getter of num_vehicles.

Returns:

The number of the vehicles

property nx_graph: Graph

Getter of the graph in Networkx format.

Returns:

A graph for a problem

static sample_most_likely(state_vector)

Compute the most likely binary string from state vector.

Parameters:

state_vector (QuasiDistribution | Statevector | 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:

ndarray

to_optimization_problem()

Represent as an optimization problem.

Convert a vehicle routing problem instance into a OptimizationProblem

Returns:

The OptimizationProblem created from the vehicle routing problem instance.

Return type:

OptimizationProblem