Publicado

2017-01-01

A comparison of trajectory granular based algorithms for the location-routing problem with heterogeneous fleet (LRPH)

Una comparación de algoritmos basados en trayectoria granular para el problema de localización y ruteo con flota heterogénea (LRPH)

Palabras clave:

Location-routing problem, heterogeneous fleet, simulated annealing, variable neighborhood search, probabilistic tabu search, metaheuristic algorithms (en)
Problema de localización y ruteo, flota heterogénea, recocido simulado, búsqueda de vecindario variable, búsqueda tabú probabilística, algoritmos metaheurísticos (es)

Autores/as

We consider the Location-Routing Problem with Heterogeneous Fleet (LRPH) in which the goal is to determine the depots to be opened, the customers to be assigned to each open depot, and the corresponding routes fulfilling the demand of the customers and by considering a heterogeneous fleet. We propose a comparison of granular approaches of Simulated Annealing (GSA), of Variable Neighborhood Search (GVNS) and of a probabilistic Tabu Search (pGTS) for the LRPH. Thus, the proposed approaches consider a subset of the search space in which non-favorable movements are discarded regarding a granularity factor. The proposed algorithms are experimentally compared for the solution of the LRPH, by taking into account the CPU time and the quality of the solutions obtained on the instances adapted from the literature. The computational results show that algorithm GSA is able to obtain high quality solutions within short CPU times, improving the results obtained by the other proposed approaches.
Nosotros consideramos el problema de localización y ruteo de vehículos con flota heterogénea (LRPH) en el cual la meta es determinar los depósitos a ser abiertos, los clientes asignados a cada deposito, y las rutas que satisfagan la demanda de los clientes considerando una flota heterogénea. Nosotros proponemos una comparación de algoritmos granulares de Recocido Simulado (GSA), Búsqueda de Vecindario Variable (GVNS) y Tabú Search probabilístico (pGTS) para el LRPH. De esta manera, los algoritmos propuestos consideran un subconjunto del espacio en el cual los movimientos menos favorables son descartados según un factor de granularidad. Los algoritmos propuestos son comparados experimentalmente para la solución del LRPH, considerando el tiempo de CPU y la calidad de la solución obtenida en instancias adaptadas de la literatura. Los resultados computacionales muestran que el algoritmos GSA es capaz de obtener buenas soluciones en tiempos computacionales reducidos, mejorando los resultados obtenidos por los otros algoritmos propuestos.

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