Please use this identifier to cite or link to this item:

http://hdl.handle.net/10609/91509
Title: Combining biased randomization with meta-heuristics for solving the multi-depot vehicle routing problem
Author: Juan Pérez, Ángel Alejandro
Barrios Barrios, Barry
Coccola, Mariana
González Martín, Sergio
Faulin Fajardo, Francisco Javier
Bektas, Tolga
Others: Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
University of Southampton
Universidad Nacional del Litoral
Keywords: vehicles
vehicle routing
heuristic algorithms
routing
Issue Date: Dec-2012
Publisher: Winter Simulation Conference (WSC). Proceedings
Citation: Juan, A.A., Barrios, B., Coccola, M., González-Martín, S., Faulin, J. & Bektas, T. (2012). Combining biased randomization with meta-heuristics for solving the multi-depot vehicle routing problem. Winter Simulation Conference (WSC). Proceedings, 2012(), 1-2. doi: 10.1109/WSC.2012.6464970
Series/Report no.: Winter Simulation Conference, Berlín, Alemanya, 9-12, desembre de 2012
Project identifier: info:eu-repo/grantAgreement/CYTED2010-511RT0419
info:eu-repo/grantAgreement/TRA2010-21644-C03
Also see: https://informs-sim.org/wsc12papers/includes/files/pos120.pdf
Abstract: 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.
Language: English
URI: http://hdl.handle.net/10609/91509
ISBN: 9781467347822
ISSN: 1558-4305
Appears in Collections:Articles

Share:
Export:
Files in This Item:
There are no files associated with this item.

Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.