Abstract
This paper evokes the vehicle routing problem (VRP) whic h aims to determine the minimumtotal cost pathways for a fleet of heterogeneous vehicles to deliver a set of customers' orders. The inability of optimization algorithms alone to fully satisfy the ne eds of logistic managers become obvious in transportation field due to the spatial nature of such problems. In this context, we couple a geographical information system (GIS) with a metaheuristic to handle the VRP e fficiently then generate a geographical solution instead of the numerical solu tion. A real-case instance in a Tunisian region is studied in order to test the proposed approach.
Highlights
INTRODUCTIONApplications that involve a distribution network are required to be illustrated as a map
In industrial companies, applications that involve a distribution network are required to be illustrated as a map
Particle swarm optimization (PSO) is a population-based evolutionary algorithm simulating the social behavior of bird flocking
Summary
Applications that involve a distribution network are required to be illustrated as a map. For this reason, the use of the GIS is recommended but this technique cannot evoke the optimization aspect of the distribution problems. Vehicle Routing Problems (VRP) introduced by Dantzig et al (1959), are the basic problems in the vehicle routing class They have been extensively studied since the sixties and have received the greatest attention in the scientific literature (i.e. Ai et al, 2009, Cheang et al, 2012, and Riera-Ledesma and Salazar-Gonzalez, 2012). According to Toth et al (2002), the largest problems that contain about 50 customers can be consistently solved by the most effective exact algorithms.
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