Only valid for pgRouting v1.x. For pgRouting v2.0 or higher see http://docs.pgrouting.org
The tsp solution is based on ordering the points using straight line (euclidean) distance between nodes. There was some thought given to pre-calculating the driving distances between the nodes using Dijkstra, but then I read a paper (unfortunately I don’t remember who wrote it), where it was proved that the quality of TSP with euclidean distance is only slightly worse that one with real distance in case of normal city layout. In case of very sparse network or rivers and bridges it becomes more inaccurate, but still wholly satisfactory. Of course it is nice to have exact solution, but this is a compromise between quality and speed (and development time also).
The TSP solver is using a genetic algorithm. It is not an exact solution, but it is guarantied that you get a solution after certain number of iterations.
The pgpsql tsp function has the following signature:
CREATE OR REPLACE FUNCTION tsp( sql text, ids varchar, source_id integer) RETURNS SETOF path_result
sql: a SQL query, which should return a set of rows with the following columns:
ids: text string containig int4 ids of vertices separated by commas
source_id: int 4 id of the start point
The function returns a set of rows. There is one row for each crossed edge, and an additional one containing the terminal vertex. The columns of each row are:
SELECT * FROM tsp('SELECT distinct source AS source_id, x1::double precision AS x, y1::double precision AS y FROM dourol WHERE source IN (83593,66059,10549,18842,13)', '83593,66059,10549,18842,13', 10549);
vertex_id | edge_id | cost -----------+---------+------ 10549 | 0 | 0 83593 | 0 | 0 66059 | 0 | 0 18842 | 0 | 0 13 | 0 | 0 (5 rows)
Afterwards vertex_id column can be used for shortest path calculation.