Ant colony system with characterization-based heuristics for a bottled-products distribution logistics system

• Autores: Claudia G. Gómez S., Laura Cruz-Reyes, Juan J. González B., Héctor J. Fraire H., Rodolfo A. Pazos R.
• Localización: Journal of computational and applied mathematics, ISSN 0377-0427, Vol. 259, Nº 2, 2014, págs. 965-977
• Idioma: inglés
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• Resumen

  • The aim of this paper is to show the solution of the Vehicle Routing Problem with Time Windows (VRPTW) as a key factor to solve a logistics system for the distribution of bottled products. We made a hybridization between an Ant Colony System algorithm (ACS) and a set of heuristics focused on instance characterization and performance learning.Wemainly propose a method to make a constrained list of candidate customers called Extended Constrained List (ECL) heuristics. Such a list is built based on the characterization of the time-window and the geographical distribution of customers. This list gives priority to the nearest customers with a smaller time window. The ECL heuristics is complemented by the Learning Levels (LL) heuristics, that allows the ants to use the pheromone matrix in two phases: local and global. In order to validate the benefits of each heuristics, a series of computational experiments were conducted using the standard Solomon�s benchmark. The experimental results show that, when the ECL heuristics is incorporated in the basic ACS algorithm, the number of required vehicles is reduced by 28.16%. When the LL heuristics is incorporated, this reduction increases to 36.83%. The experimentation reveals that, by a suitable characterization, preexisting conditions in the instances are identified in order to take advantage of both of the ECL and LL.