Keyword(s):Multi-depot vehicle routing problem, Ant colony optimization, Genetic algorithm.

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Ontology
Subjects/Areas/Topics:Artificial Intelligence
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Computational Intelligence
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Evolutionary Computing
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Genetic Algorithms
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Informatics in Control, Automation and Robotics
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Intelligent Control Systems and Optimization
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Soft Computing

Abstract: This paper addresses the multi-depot vehicle routing problem. This problem involves designing a set of routes in order to deliver goods from several depots to a set of geographically dispersed customers. For solving this problem, we propose two different approaches. Both have in common the use of an Ant Colony Optimization algorithm to construct the routes from each depot. The approaches differ in the manner in which depots are dealt with in terms of how customers are assigned to depots. In the first method, called ACO-MDVRP, the customer assignment process is controlled by the ant colony by adding a super-depot which is connected with each depot by arcs with zero unit cost. The second method, called GA-MDVRP, is a hybrid algorithm in the sense that an Ant Colony Optimization algorithm is embedded in a genetic algorithm. In order to construct a feasible solution, the procedure uses a genetic algorithm to assign customers to depots. Then, under the given data on each depot, the corresponding vehicle routing problems are solved by using Ant Colony Optimization.(More)

This paper addresses the multi-depot vehicle routing problem. This problem involves designing a set of routes in order to deliver goods from several depots to a set of geographically dispersed customers. For solving this problem, we propose two different approaches. Both have in common the use of an Ant Colony Optimization algorithm to construct the routes from each depot. The approaches differ in the manner in which depots are dealt with in terms of how customers are assigned to depots. In the first method, called ACO-MDVRP, the customer assignment process is controlled by the ant colony by adding a super-depot which is connected with each depot by arcs with zero unit cost. The second method, called GA-MDVRP, is a hybrid algorithm in the sense that an Ant Colony Optimization algorithm is embedded in a genetic algorithm. In order to construct a feasible solution, the procedure uses a genetic algorithm to assign customers to depots. Then, under the given data on each depot, the corresponding vehicle routing problems are solved by using Ant Colony Optimization.

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I. Calvete, H.; Galé, C. and J. Oliveros, M. (2011). EVOLUTIVE AND ACO STRATEGIES FOR SOLVING THE MULTI-DEPOT VEHICLE ROUTING PROBLEM.In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011) ISBN 978-989-8425-83-6, pages 73-79. DOI: 10.5220/0003673400730079

@conference{ecta11, author={H. I. Calvete. and C. Galé. and M. J. Oliveros.}, title={EVOLUTIVE AND ACO STRATEGIES FOR SOLVING THE MULTI-DEPOT VEHICLE ROUTING PROBLEM}, booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011)}, year={2011}, pages={73-79}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0003673400730079}, isbn={978-989-8425-83-6}, }

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JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011) TI - EVOLUTIVE AND ACO STRATEGIES FOR SOLVING THE MULTI-DEPOT VEHICLE ROUTING PROBLEM SN - 978-989-8425-83-6 AU - I. Calvete, H. AU - Galé, C. AU - J. Oliveros, M. PY - 2011 SP - 73 EP - 79 DO - 10.5220/0003673400730079