TY - GEN

T1 - An Ant Colony Optimization algorithm for solving the fixed destination multi-depot multiple traveling salesman problem with non-random parameters

AU - Ramadhani, T.

AU - Hertono, Gatot Fatwanto

AU - Handari, Bevina Desjwiandra

PY - 2017/7/10

Y1 - 2017/7/10

N2 - The Multiple Traveling Salesman Problem (MTSP) is the extension of the Traveling Salesman Problem (TSP) in which the shortest routes of m salesmen all of which start and finish in a single city (depot) will be determined. If there is more than one depot and salesmen start from and return to the same depot, then the problem is called Fixed Destination Multi-depot Multiple Traveling Salesman Problem (MMTSP). In this paper, MMTSP will be solved using the Ant Colony Optimization (ACO) algorithm. ACO is a metaheuristic optimization algorithm which is derived from the behavior of ants in finding the shortest route(s) from the anthill to a form of nourishment. In solving the MMTSP, the algorithm is observed with respect to different chosen cities as depots and non-randomly three parameters of MMTSP: m, K, L, those represents the number of salesmen, the fewest cities that must be visited by a salesman, and the most number of cities that can be visited by a salesman, respectively. The implementation is observed with four dataset from TSPLIB. The results show that the different chosen cities as depots and the three parameters of MMTSP, in which m is the most important parameter, affect the solution.

AB - The Multiple Traveling Salesman Problem (MTSP) is the extension of the Traveling Salesman Problem (TSP) in which the shortest routes of m salesmen all of which start and finish in a single city (depot) will be determined. If there is more than one depot and salesmen start from and return to the same depot, then the problem is called Fixed Destination Multi-depot Multiple Traveling Salesman Problem (MMTSP). In this paper, MMTSP will be solved using the Ant Colony Optimization (ACO) algorithm. ACO is a metaheuristic optimization algorithm which is derived from the behavior of ants in finding the shortest route(s) from the anthill to a form of nourishment. In solving the MMTSP, the algorithm is observed with respect to different chosen cities as depots and non-randomly three parameters of MMTSP: m, K, L, those represents the number of salesmen, the fewest cities that must be visited by a salesman, and the most number of cities that can be visited by a salesman, respectively. The implementation is observed with four dataset from TSPLIB. The results show that the different chosen cities as depots and the three parameters of MMTSP, in which m is the most important parameter, affect the solution.

UR - http://www.scopus.com/inward/record.url?scp=85026218723&partnerID=8YFLogxK

U2 - 10.1063/1.4991227

DO - 10.1063/1.4991227

M3 - Conference contribution

AN - SCOPUS:85026218723

T3 - AIP Conference Proceedings

BT - International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016

A2 - Sugeng, Kiki Ariyanti

A2 - Triyono, Djoko

A2 - Mart, Terry

PB - American Institute of Physics Inc.

T2 - 2nd International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016

Y2 - 1 November 2016 through 2 November 2016

ER -