Giving a set of points and the distance each pair of points, found the roite which go through the points minimizing the distance traveled.

More information about the TSP, the used algorithm belongs to the family of the genetic algorithms.

These do not ensure to find the optimal solution, but they find suboptimal solutions in a reasonable time.

1 geo.tspSolver

<geo.tspSolver
    lat_col='lat_col'
    lng_col='lng_col'
    id_col='id_col'
    origin_city_code='origin_city_code'
    end_city_code='end_city_code'
    is_closed='is_closed'
    population='population'
    generations='generations'
    mutation_rate='mutation_rate'
>
    <v_cities /> +
    <v_distances /> +
</geo.tspSolver>

Attributes
Name	Type	Required	Default	Description
Alat_col	integer				Index of the column which contains the latitude in the ResultSet.
Alng_col	integer				Index of the column which contains the length in the ResultSet.
Aid_col	integer				Index of the column which contains the identifier of the row in the ResultSet.
Aorigin_city_code	string				Identifier of the TSP starting point.
Aend_city_code	string				Identifier of the TSP ending point.
Ais_closed	boolean				Allows to indicate which the travel must be closed. The route will be optimized aking into account that after visiting the final point, you must return to the starting point.
Apopulation	integer				Parameter used in the genetic algorithm.
Agenerations	integer				Parameter used in the genetic algorithm.
Amutation_rate	decimal				Parameter used in the genetic algorithm.

Arguments
Name	Type	Required	Unique	Nullable	Description
Ev_cities	vtable				Vtable of the points for which the TSP has to pass. It is required a column with the latitude (or coordinate x=, other with the lenght (or coordinate y) and a column with the id of the point. By using the attributes lat_col, lng:col, id_col the index of this columns is indicated in the resultSet.
Ev_distances	vtable				Vtable NxN with the array of distances between the points of the problem.

Returns
Type	Description
Vtable	Returns Vtable with the columns "lat", "lng", "name". The order of the points is the solution found using the genetic algorithm.

Example

Define a StringBuffer with a string.

Copy

<xsql-script name='stringbuffer_sample'>
    <body>	
 <println><v_cities /></println>	
 	    	
+-------+-------+---------------+
|gps_lat|gps_lon|assign_customer|
|decimal|decimal|char           |
+-------+-------+---------------+
|41,3712| 2,1114|10018          |
|41,3816| 2,1625|10006          |
|41,3819| 2,1578|10007          |
|41,3835| 2,1475|10004          |
|41,3895| 2,1499|10003          |
|41,3946| 2,1545|10009          |
|41,3946| 2,1525|10002          |
|41,3963| 2,1578|10011          |
|41,4045| 2,1571|10010          |
|41,4182| 2,1811|10012          |
|41,3941| 2,1923|T1000          |
+-------+-------+---------------+
    
 
  <println><v_distances /></println>	
   
+--------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+
|codcli_1|pivot11   |pivot1    |pivot2    |pivot3    |pivot4    |pivot5    |pivot6    |pivot7    |pivot8    |pivot9    |pivot10   |
|char    |decimal   |decimal   |decimal   |decimal   |decimal   |decimal   |decimal   |decimal   |decimal   |decimal   |decimal   |
|<null>  |10002     |10003     |10004     |10006     |10007     |10009     |10010     |10011     |10012     |10018     |T1000     |
|        |route_dist|route_dist|route_dist|route_dist|route_dist|route_dist|route_dist|route_dist|route_dist|route_dist|route_dist|
+--------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+
|10002   |          |   1110,00|   2031,00|   2457,00|   2463,00|    965,00|   2143,00|    776,00|   4651,00|   6573,00|   1240,00|
|10003   |    984,00|          |   1388,00|   2423,00|   1904,00|    931,00|   2796,00|   2272,00|   6147,00|   5938,00|   1693,00|
|10004   |   2234,00|   1575,00|          |   1765,00|   1234,00|   1964,00|   4362,00|   2995,00|   6870,00|   4689,00|   1506,00|
|10006   |   1974,00|   1653,00|   2031,00|          |   1358,00|   1921,00|   3848,00|   2537,00|   5945,00|   5664,00|   1349,00|
|10007   |   2063,00|   1629,00|   1466,00|   1074,00|          |   2011,00|   3824,00|   2513,00|   5921,00|   5375,00|   1877,00|
|10009   |    851,00|    944,00|   1865,00|   2291,00|   2297,00|          |   2329,00|   1018,00|   4328,00|   6407,00|   1074,00|
|10010   |   2346,00|   2805,00|   3247,00|   3677,00|   3684,00|   2666,00|          |   1342,00|   3908,00|   7565,00|   2461,00|
|10011   |   1004,00|   1463,00|   2708,00|   2815,00|   2692,00|   1324,00|   1756,00|          |   4492,00|   6223,00|   1470,00|
|10012   |   5047,00|   5506,00|   6751,00|   5941,00|   5947,00|   5367,00|   3269,00|   3532,00|          |  10265,00|   4724,00|
|10018   |   6134,00|   4513,00|   4091,00|   5373,00|   4843,00|   4902,00|   7822,00|   6895,00|   9960,00|          |   6720,00|
|T1000   |   1108,00|   1205,00|   1583,00|   1209,00|   1216,00|   1055,00|   2850,00|   1539,00|   4845,00|   6037,00|          |
+--------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+    

    	
        <!-- SOLVE THE PROBLEM -->
        <vtable name='v_tsp_result'>
            <in>
                <geo.tspSolver  lat_col="1" lng_col="2" id_col="3" population="5000" generations="1000" mutation_rate="0.025" origin_city_code="T1000">
                    <cities />
                    <pivoted />
                </geo.tspSolver>
            </in>
        </vtable>    	

<println><v_tsp_result /></pritnnln>
+-------+------+-----+
|lat    |lng   |name |
|double |double|char |
+-------+------+-----+
|41,3941|2,1923|T1000|
|41,3819|2,1578|10007|
|41,3816|2,1625|10006|
|41,3946|2,1545|10009|
|41,4182|2,1811|10012|
|41,4045|2,1571|10010|
|41,3963|2,1578|10011|
|41,3946|2,1525|10002|
|41,3895|2,1499|10003|
|41,3835|2,1475|10004|
|41,3712|2,1114|10018|
+-------+------+-----+

    </body>
</xsql-script>