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dc.contributor.authorSicilia, Juan Antonio
dc.contributor.authorRoyo-Agustin, Beatriz
dc.contributor.authorQuemada-mayoral, Carlos
dc.contributor.authorOlivares-Colay, María José
dc.contributor.authorLarrodé, Emilio
dc.description.abstractThis paper presents an original tool to optimize the long-distance freight transport by road based on an original methodology for allocating communication roads depending on the volume of orders. The goal this system consists of helping traffic managers of transport companies to achieve the optimal distribution in order to reduce operational costs satisfying the service quality, optimizing the route and the loading of vehicles and grouping orders following proper load/unload procedures. The system developed is an intuitive application that enables a fast learning of the existing functionalities adapting in a very versatile way to any case study. An algorithm based on the metaheuristic, ant colony optimization, is used to solve the problem taking into account the following specific characteristics: there is a maximum driving time per day, the vehicle capacity is limited, there are compatibility constraint of different types of goods in the same vehicle, customers have time constraints to pickup/deliver goods and the procedure of loading/unloading is LIFO (Last Input First Output). The efficiency of the algorithm has been proved using data from real problems. The basis of our computational experiments is the historical data of an important freight transport company.es_ES
dc.relation.ispartofseries;vol. 90, nº 1
dc.subjectlong haul transportationes_ES
dc.subjectless than truckloades_ES
dc.subjectvehicle routing problemes_ES
dc.subjectant colony optimization metaheuristices_ES
dc.subjectdecision support systemes_ES
dc.titleAn decision support system to long haul freight transportation by means of ant colony optimizationes_ES
dc.title.alternativeSistema de apoyo en la toma de decisiones para el transporte de mercancías de larga distancia por medio de la optimización de colonia de hormigases_ES
dc.typeArticulo Revista Indexadaes_ES

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