An Ant Colony System Algorithm to Solve Routing Problems Applied to the Delivery of Bottled Products

This work presents a methodology of solution for the well-known vehicle routing problem (VRP) based on an ant colony system heuristic algorithm (ACS), which is applied to optimize the delivery process of RoSLoP (Routing-Scheduling-Loading Problem) identified in the company case of study. A first version of this algorithm models six variants of VRP and its solution satisfies the 100% of demands of the customers. The new version of the algorithm can solve 11 variants of VRP as a rich VRP. Experiments were carried out with real instances. The new algorithm shows a saving of two vehicles with regard to the first version, reducing the operation costs of the company. These results prove the viability of using heuristic methods and optimization techniques to develop new software applications.

[1]  Hui Gao,et al.  Parallel and Distributed Processing and Applications , 2005 .

[2]  Richard F. Hartl,et al.  Analyzing a Unified Ant System for the VRP and Some of Its Variants , 2003, EvoWorkshops.

[3]  Principles and Practice of Constraint Programming — CP98 , 1999, Lecture Notes in Computer Science.

[4]  Russell R. Barton,et al.  Institute for Operations Research and the Management Sciences , 2003, INFORMS J. Comput..

[5]  Charlotte Jacobs-Blecha.  The Vehicle Routing Problem with Backhauls: Properties and Solution Algorithms , 1998 .

[6]  Michel Gendreau,et al.  A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows , 1997, Transp. Sci..

[7]  Jean-Yves Potvin,et al.  An Exchange Heuristic for Routeing Problems with Time Windows , 1995 .

[8]  Éric D. Taillard,et al.  A heuristic column generation method for the heterogeneous fleet VRP , 1999, RAIRO Oper. Res..

[9]  Paolo Toth,et al.  Models, relaxations and exact approaches for the capacitated vehicle routing problem , 2002, Discret. Appl. Math..

[10]  Alexander Gelbukh,et al.  MICAI 2007: Advances in Artificial Intelligence, 6th Mexican International Conference on Artificial Intelligence, Aguascalientes, Mexico, November 4-10, 2007, Proceedings , 2007, MICAI.

[11]  Rolf Drechsler,et al.  Applications of Evolutionary Computing, EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings , 2008, EvoWorkshops.

[12]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[13]  G. Croes.  A Method for Solving Traveling-Salesman Problems , 1958 .

[14]  David Pisinger,et al.  A general heuristic for vehicle routing problems , 2007, Comput. Oper. Res..

[15]  M. G. Speranza,et al.  The vehicle routing problem with capacity 2 and 3, general distances and multiple customer visits , 2001 .

[16]  V. GracielaMoraGuadalupeCastilla,et al.  DiPro: An Algorithm for the Packing in Product Transportation Problems with Multiple Loading and Routing Variants , 2007, MICAI.

[17]  Paolo Toth,et al.  The Vehicle Routing Problem , 2002, SIAM monographs on discrete mathematics and applications.

[18]  Paul Shaw,et al.  Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems , 1998, CP.

[19]  Nelson Rangel-Valdez,et al.  A Distributed Metaheuristic for Solving a Real-World Scheduling-Routing-Loading Problem , 2007, ISPA.