Título:
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Combining biased randomization with meta-heuristics for solving the multi-depot vehicle routing problem
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Autor/a:
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Juan Pérez, Ángel Alejandro; Barrios Barrios, Barry; Coccola, Mariana; González Martín, Sergio; Faulin Fajardo, Francisco Javier; Bektas, Tolga
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Abstract:
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This paper proposes a hybrid algorithm, combining Biased-Randomized (BR) processes with an Iterated Local Search (ILS) meta-heuristic, to solve the Multi-Depot Vehicle Routing Problem (MDVRP). Our approach assumes a scenario in which each depot has unlimited service capacity and in which all vehicles are identical (homogeneous fleet). During the routing process, however, each vehicle is assumed to have a limited capacity. Two BR processes are employed at different stages of the ILS procedure in order to: (a) define the perturbation operator, which generates new assignment maps by associating customers to depots in a biased-random way according to a distance-based criterion; and (b) generate good routing solutions for each customers-depots assignment map. These biased-randomization processes rely on the use of a pseudo-geometric probability distribution. Our approach does not need from fine-tuning processes which usually are complex and time consuming. Some preliminary tests have been carried out already with encouraging results. |
Materia(s):
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-Universidad Pública de Navarra -vehicles -vehicle routing -heuristic algorithms -routing -enrutament -vehicles -enrutament de vehicles -algorismes heurístics -enrutamiento -enrutamiento -algoritmos heurísticos -enrutamiento de vehículos -Algorithms -Algorismes -Algoritmos |
Derechos:
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(c) Author/s & (c) Journal
info:eu-repo/semantics/restrictedAccess |
Tipo de documento:
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Objeto de conferencia |
Editor:
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Winter Simulation Conference (WSC). Proceedings
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