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A DECISION SUPPORT SYSTEM TO LONG HAUL FREIGHT TRANSPORTATION BY MEANS OF ANT COLONY OPTIMIZATION

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JANUARY 2015   -  Volume: 90 -  Pages: 105-113

DOI:

https://doi.org/10.6036/7190

Authors:

JUAN ANTONIO SICILIA MONTALVO - BEATRIZ ROYO AGUSTIN - CARLOS QUEMADA MAYORAL - MARIA JOSE OLIVEROS COLAY - EMILIO LARRODE PELLICER

Disciplines:

  • Transportation systems technology (OTRAS )

Downloads:   199

Cites in Web of Science:  4

How to cite this paper:  
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Received Date :   23 June 2014

Accepted Date :   14 November 2014


Key words:
Transporte de larga distancia, transporte de carga fraccionada, problema de planificación de rutas, metaheurística de optimización de colonia de hormigas, sistema de ayuda decisional, Long haul transportation, less than truckload, vehicle routing problem, ant colony optimization metaheuristic, decision support system
Article type:
ARTICULO DE INVESTIGACION / RESEARCH ARTICLE
Section:
RESEARCH ARTICLES

This paper presents an original tool to optimize the long-distance freight transport by road in the Iberian Peninsula among a large number of customers.
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 Out).

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.

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