qiskit_optimization.applications.vehicle_routing のソースコード

# This code is part of a Qiskit project.
#
# (C) Copyright IBM 2018, 2023.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
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"""An application class for the vehicle routing problem."""

import itertools
import random
from typing import List, Dict, Union, Optional

import networkx as nx
import numpy as np
from docplex.mp.model import Model

from qiskit_optimization.algorithms import OptimizationResult
from qiskit_optimization.problems.quadratic_program import QuadraticProgram
from qiskit_optimization.translators import from_docplex_mp
from .graph_optimization_application import GraphOptimizationApplication


[ドキュメント]class VehicleRouting(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 """ def __init__( self, graph: Union[nx.Graph, np.ndarray, List], num_vehicles: int = 2, depot: int = 0, ) -> None: """ Args: graph: A graph representing a problem. It can be specified directly as a `NetworkX <https://networkx.org/>`_ graph, or as an array or list format suitable to build out a NetworkX graph. num_vehicles: The number of vehicles depot: The index of the depot node where all the vehicle depart """ super().__init__(graph) self._num_vehicles = num_vehicles self._depot = depot
[ドキュメント] def to_quadratic_program(self) -> QuadraticProgram: """Convert a vehicle routing problem instance into a :class:`~qiskit_optimization.problems.QuadraticProgram` Returns: The :class:`~qiskit_optimization.problems.QuadraticProgram` created from the vehicle routing problem instance. """ mdl = Model(name="Vehicle routing") n = self._graph.number_of_nodes() x = {} for i in range(n): for j in range(n): if i != j: x[(i, j)] = mdl.binary_var(name=f"x_{i}_{j}") mdl.minimize( mdl.sum( self._graph.edges[i, j]["weight"] * x[(i, j)] for i in range(n) for j in range(n) if i != j ) ) # Only 1 edge goes out from each node for i in range(n): if i != self.depot: mdl.add_constraint(mdl.sum(x[i, j] for j in range(n) if i != j) == 1) # Only 1 edge comes into each node for j in range(n): if j != self.depot: mdl.add_constraint(mdl.sum(x[i, j] for i in range(n) if i != j) == 1) # For the depot node mdl.add_constraint( mdl.sum(x[i, self.depot] for i in range(n) if i != self.depot) == self.num_vehicles ) mdl.add_constraint( mdl.sum(x[self.depot, j] for j in range(n) if j != self.depot) == self.num_vehicles ) # To eliminate sub-routes node_list = [i for i in range(n) if i != self.depot] clique_set = [] for i in range(2, len(node_list) + 1): for comb in itertools.combinations(node_list, i): clique_set.append(list(comb)) for clique in clique_set: mdl.add_constraint( mdl.sum(x[(i, j)] for i in clique for j in clique if i != j) <= len(clique) - 1 ) op = from_docplex_mp(mdl) return op
[ドキュメント] def interpret(self, result: Union[OptimizationResult, np.ndarray]) -> List[List[List[int]]]: """Interpret a result as a list of the routes for each vehicle Args: result : The calculated result of the problem Returns: A list of the routes for each vehicle """ x = self._result_to_x(result) n = self._graph.number_of_nodes() idx = 0 edge_list = [] for i in range(n): for j in range(n): if i != j: if x[idx]: edge_list.append([i, j]) idx += 1 route_list = [] # type: List[List[List[int]]] for k in range(self.num_vehicles): i = 0 start = self.depot route_list.append([]) while i < len(edge_list): if edge_list[i][0] == start: if edge_list[i][1] == self.depot: # If a loop is completed route_list[k].append(edge_list.pop(i)) break # Move onto the next edge start = edge_list[i][1] route_list[k].append(edge_list.pop(i)) i = 0 continue i += 1 if edge_list: route_list.append(edge_list) return route_list
def _draw_result( self, result: Union[OptimizationResult, np.ndarray], pos: Optional[Dict[int, np.ndarray]] = None, ) -> None: """Draw the result with colors Args: result: The calculated result for the problem pos: The positions of nodes """ import matplotlib as mpl route_list = self.interpret(result) nx.draw(self._graph, with_labels=True, pos=pos) nx.draw_networkx_edges( self._graph, pos, edgelist=self._edgelist(route_list), width=8, alpha=0.5, edge_color=self._edge_color(route_list), edge_cmap=mpl.colormaps["plasma"], ) def _edgelist(self, route_list: List[List[List[int]]]): # Arrange route_list and return the list of the edges for the edge list of # nx.draw_networkx_edges return [edge for k in range(len(route_list)) for edge in route_list[k]] def _edge_color(self, route_list: List[List[List[int]]]): # Arrange route_list and return the list of the colors of each route # for edge_color of nx.draw_networkx_edges return [k / len(route_list) for k in range(len(route_list)) for edge in route_list[k]] @property def num_vehicles(self) -> int: """Getter of num_vehicles Returns: The number of the vehicles """ return self._num_vehicles @num_vehicles.setter def num_vehicles(self, num_vehicles: int) -> None: """Setter of num_vehicles Args: num_vehicles: The number of vehicle """ self._num_vehicles = num_vehicles @property def depot(self) -> int: """Getter of depot Returns: The node index of the depot where all the vehicles depart """ return self._depot @depot.setter def depot(self, depot: int) -> None: """Setter of depot Args: depot: The node index of the depot where all the vehicles depart """ self._depot = depot
[ドキュメント] @staticmethod # pylint: disable=undefined-variable def create_random_instance( n: int, low: int = 0, high: int = 100, seed: Optional[int] = None, num_vehicle: int = 2, depot: int = 0, ) -> "VehicleRouting": """Create a random instance of the vehicle routing problem. Args: n: the number of nodes. low: The minimum value for the coordinate of a node. high: The maximum value for the coordinate of a node. seed: the seed for the random coordinates. num_vehicle: The number of the vehicles depot: The index of the depot node where all the vehicle depart Returns: A VehicleRouting instance created from the input information """ random.seed(seed) pos = {i: (random.randint(low, high), random.randint(low, high)) for i in range(n)} graph = nx.random_geometric_graph(n, np.hypot(high - low, high - low) + 1, pos=pos) for w, v in graph.edges: delta = [graph.nodes[w]["pos"][i] - graph.nodes[v]["pos"][i] for i in range(2)] graph.edges[w, v]["weight"] = np.rint(np.hypot(delta[0], delta[1])) return VehicleRouting(graph, num_vehicle, depot)