147 lines
5.0 KiB
Python
Executable File
147 lines
5.0 KiB
Python
Executable File
#!/usr/bin/env python
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# This Python file uses the following encoding: utf-8
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# Copyright 2015 Tin Arm Engineering AB
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# Copyright 2018 Google LLC
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Travelling Salesman Problem (TSP).
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This is a sample using the routing library python wrapper to solve a TSP
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problem.
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A description of the problem can be found here:
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http://en.wikipedia.org/wiki/Travelling_salesman_problem.
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Distances are in meters.
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"""
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from __future__ import print_function
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from collections import namedtuple
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from six.moves import xrange
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from ortools.constraint_solver import pywrapcp
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from ortools.constraint_solver import routing_enums_pb2
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###########################
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# Problem Data Definition #
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###########################
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# City block declaration
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CityBlock = namedtuple('CityBlock', ['width', 'height'])
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class DataProblem():
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"""Stores the data for the problem"""
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def __init__(self):
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"""Initializes the data for the problem"""
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# Locations in block unit
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locations = \
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[(4, 4), # depot
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(2, 0), (8, 0), # locations to visit
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(0, 1), (1, 1),
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(5, 2), (7, 2),
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(3, 3), (6, 3),
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(5, 5), (8, 5),
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(1, 6), (2, 6),
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(3, 7), (6, 7),
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(0, 8), (7, 8)]
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# Compute locations in meters using the block dimension defined as follow
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# Manhattan average block: 750ft x 264ft -> 228m x 80m
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# here we use: 114m x 80m city block
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# src: https://nyti.ms/2GDoRIe "NY Times: Know Your distance"
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city_block = CityBlock(width=228/2, height=80)
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self._locations = [(loc[0] * city_block.width, loc[1] * city_block.height)
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for loc in locations]
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@property
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def locations(self):
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"""Gets locations"""
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return self._locations
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@property
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def num_locations(self):
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"""Gets number of locations"""
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return len(self.locations)
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@property
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def depot(self):
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"""Gets depot location node index"""
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return 0
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#######################
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# Problem Constraints #
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#######################
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def manhattan_distance(position_1, position_2):
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"""Computes the Manhattan distance between two points"""
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return (
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abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
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class CreateDistanceEvaluator(object): # pylint: disable=too-few-public-methods
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"""Creates callback to return distance between points."""
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def __init__(self, data):
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"""Initializes the distance matrix."""
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self._distances = {}
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# precompute distance between location to have distance callback in O(1)
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for from_node in xrange(data.num_locations):
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self._distances[from_node] = {}
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for to_node in xrange(data.num_locations):
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if from_node == to_node:
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self._distances[from_node][to_node] = 0
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else:
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self._distances[from_node][to_node] = (
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manhattan_distance(data.locations[from_node],
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data.locations[to_node]))
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def distance_evaluator(self, from_node, to_node):
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"""Returns the manhattan distance between the two nodes"""
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return self._distances[from_node][to_node]
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###########
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# Printer #
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###########
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def print_solution(routing, assignment):
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"""Prints assignment on console"""
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print('Objective: {}'.format(assignment.ObjectiveValue()))
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index = routing.Start(0)
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plan_output = 'Route:\n'
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distance = 0
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while not routing.IsEnd(index):
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plan_output += ' {} -> '.format(routing.IndexToNode(index))
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previous_index = index
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index = assignment.Value(routing.NextVar(index))
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distance += routing.GetArcCostForVehicle(previous_index, index, 0)
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plan_output += ' {}\n'.format(routing.IndexToNode(index))
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plan_output += 'Distance of the route: {}m\n'.format(distance)
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print(plan_output)
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########
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# Main #
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########
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def main():
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"""Entry point of the program"""
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# Instantiate the data problem.
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data = DataProblem()
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# Create Routing Model
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routing = pywrapcp.RoutingModel(data.num_locations, 1, data.depot)
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# Define weight of each edge
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distance_evaluator = CreateDistanceEvaluator(data).distance_evaluator
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routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator)
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# Setting first solution heuristic (cheapest addition).
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search_parameters = pywrapcp.RoutingModel.DefaultSearchParameters()
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search_parameters.first_solution_strategy = (
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routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # pylint: disable=no-member
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# Solve the problem.
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assignment = routing.SolveWithParameters(search_parameters)
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print_solution(routing, assignment)
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if __name__ == '__main__':
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main()
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