The vehicle routing problem
Analyzing a unified ant system for the VRP and some of its variants
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
A distributed metaheuristic for solving a real-world scheduling-routing-loading problem
ISPA'07 Proceedings of the 5th international conference on Parallel and Distributed Processing and Applications
Journal of Computational and Applied Mathematics
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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.