• Supported versions:

pgr_TSPeuclidean

  • pgr_TSPeuclidean - Aproximation using metric algorithm.

_images/boost-inside.jpeg

Boost Graph Inside

Availability:

  • Version 3.2.1

    • Metric Algorithm from Boost library

    • Simulated Annealing Algorithm no longer supported

      • The Simulated Annealing Algorithm related parameters are ignored: max_processing_time, tries_per_temperature, max_changes_per_temperature, max_consecutive_non_changes, initial_temperature, final_temperature, cooling_factor, randomize

  • Versión 3.0.0

    • Cambio de nombre de pgr_eucledianTSP

  • Versión 2.3.0

    • Nueva función Oficial

Descripción

Problem Definition

The travelling salesperson problem (TSP) asks the following question:

Dada una lista de ciudades y las distancias entre cada par de ciudades, que corresponde con la ruta más corta posible que para visite cada ciudad exactamente una vez y regrese a la ciudad de origen?

General Characteristics

  • This problem is an NP-hard optimization problem.

  • Metric Algorithm is used

  • Implementation generates solutions that are twice as long as the optimal tour in the worst case when:

    • Graph is undirected

    • Graph is fully connected

    • Graph where traveling costs on edges obey the triangle inequality.

  • On an undirected graph:

    • The traveling costs are symmetric:

    • Traveling costs from u to v are just as much as traveling from v to u

Characteristics

  • Duplicated identifiers with different coordinates are not allowed

    • The coordinates are quite the same for the same identifier, for example

      1, 3.5, 1
1, 3.499999999999 0.9999999

    • The coordinates are quite different for the same identifier, for example

      2 , 3.5, 1.0
2 , 3.6, 1.1

    • Any duplicated identifier will be ignored. The coordinates that will be kept is arbitrarly.

Firmas

Resumen

pgr_TSPeuclidean(Coordinates SQL, [start_id], [end_id])
RETURNS SETOF (seq, node, cost, agg_cost)
Ejemplo

With default values

SELECT * FROM pgr_TSPeuclidean(
    $$
    SELECT id, st_X(the_geom) AS x, st_Y(the_geom)AS y  FROM edge_table_vertices_pgr
    $$);
 seq | node |      cost      |   agg_cost
-----+------+----------------+---------------
   1 |    1 |              0 |             0
   2 |    2 |              1 |             1
   3 |    8 |  1.41421356237 | 2.41421356237
   4 |    7 |              1 | 3.41421356237
   5 |   14 |  1.58113883008 | 4.99535239246
   6 |   15 |            1.5 | 6.49535239246
   7 |   13 |            0.5 | 6.99535239246
   8 |   17 |            1.5 | 8.49535239246
   9 |   12 |  1.11803398875 | 9.61338638121
  10 |    9 |              1 | 10.6133863812
  11 |   16 | 0.583095189485 | 11.1964815707
  12 |    6 | 0.583095189485 | 11.7795767602
  13 |   11 |              1 | 12.7795767602
  14 |   10 |              1 | 13.7795767602
  15 |    5 |              1 | 14.7795767602
  16 |    4 |   2.2360679775 | 17.0156447377
  17 |    3 |              1 | 18.0156447377
  18 |    1 |  1.41421356237 |    19.4298583
(18 rows)

Parámetros

Parámetro

Tipo

Valores predeterminados

Descripción

Coordenadas SQL

TEXT

An SQL query, described in the Coordinates SQL section

start_vid

BIGINT

0

The first visiting vertex

  • When 0 any vertex can become the first visiting vertex.

end_vid

BIGINT

0

Last visiting vertex before returning to start_vid.

  • When 0 any vertex can become the last visiting vertex before returning to start_vid.

  • When NOT 0 and start_vid = 0 then it is the first and last vertex

Consulta interna

Coordinates SQL

Coordenadas SQL: una consulta SQL, que debe devolver un conjunto de filas con las siguientes columnas:

Columna

Tipo

Descripción

id

ANY-INTEGER

Identifier of the starting vertex.

x

ANY-NUMERICAL

Valor X de la coordenada.

y

ANY-NUMERICAL

Valor Y de la coordenada.

Columnas de Resultados

Devuelve el CONJUNTO DE (seq, node, cost, agg_cost)

Columna

Tipo

Descripción

seq

INTEGER

Secuencia de filas.

node

BIGINT

Identificador del nodo/coordenada/punto.

cost

FLOAT

Coste que se debe recorrer desde el nodo al siguiente nodo en la secuencia de ruta.

  • 0 for the last row in the tour sequence.

agg_cost

FLOAT

Costo agregado del nodo en seq = 1 hacia el nodo actual.

  • 0 for the first row in the tour sequence.

Ejemplos Adicionales

Ejemplo

Test 29 cities of Western Sahara

This example shows how to make performance tests using University of Waterloo’s example data using the 29 cities of Western Sahara dataset

Creating a table for the data and storing the data

CREATE TABLE wi29 (id BIGINT, x FLOAT, y FLOAT, geom geometry);
INSERT INTO wi29 (id, x, y) VALUES
(1,20833.3333,17100.0000),
(2,20900.0000,17066.6667),
(3,21300.0000,13016.6667),
(4,21600.0000,14150.0000),
(5,21600.0000,14966.6667),
(6,21600.0000,16500.0000),
(7,22183.3333,13133.3333),
(8,22583.3333,14300.0000),
(9,22683.3333,12716.6667),
(10,23616.6667,15866.6667),
(11,23700.0000,15933.3333),
(12,23883.3333,14533.3333),
(13,24166.6667,13250.0000),
(14,25149.1667,12365.8333),
(15,26133.3333,14500.0000),
(16,26150.0000,10550.0000),
(17,26283.3333,12766.6667),
(18,26433.3333,13433.3333),
(19,26550.0000,13850.0000),
(20,26733.3333,11683.3333),
(21,27026.1111,13051.9444),
(22,27096.1111,13415.8333),
(23,27153.6111,13203.3333),
(24,27166.6667,9833.3333),
(25,27233.3333,10450.0000),
(26,27233.3333,11783.3333),
(27,27266.6667,10383.3333),
(28,27433.3333,12400.0000),
(29,27462.5000,12992.2222);

Adding a geometry (for visual purposes)

UPDATE wi29 SET geom = ST_makePoint(x,y);

Getting a total cost of the tour, compare the value with the length of an optimal tour is 27603, given on the dataset

SELECT *
FROM pgr_TSPeuclidean($$SELECT * FROM wi29$$)
WHERE seq = 30;
 seq | node |     cost      |   agg_cost
-----+------+---------------+---------------
  30 |    1 | 2266.91173136 | 28777.4854127
(1 row)

Getting a geometry of the tour

WITH
tsp_results AS (SELECT seq, geom FROM pgr_TSPeuclidean($$SELECT * FROM wi29$$) JOIN wi29 ON (node = id))
SELECT ST_MakeLine(ARRAY(SELECT geom FROM tsp_results ORDER BY seq));
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    st_makeline
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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
(1 row)

Visualy, The first image is the optimal solution and the second image is the solution obtained with pgr_TSPeuclidean.

_images/wi29optimal.png _images/wi29Solution.png

Ver también

Índices y tablas