The vehicle routing problem
DQL: A New Updating Strategy for Reinforcement Learning Based on Q-Learning
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
A Reactive Variable Neighborhood Search for the Vehicle-Routing Problem with Time Windows
INFORMS Journal on Computing
Ant colony optimization theory: a survey
Theoretical Computer Science
A general heuristic for vehicle routing problems
Computers and Operations Research
Formulations and exact algorithms for the vehicle routing problem with time windows
Computers and Operations Research
A New Approach to Improve the Ant Colony System Performance: Learning Levels
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
An ant colony system algorithm to solve routing problems applied to the delivery of bottled products
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Using feedback in collaborative reinforcement learning to adaptively optimize MANET routing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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. We mainly 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.