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|Title:||Combining biased randomization with meta-heuristics for solving the multi-depot vehicle routing problem|
|Author:||Juan Pérez, Ángel Alejandro|
Barrios Barrios, Barry
González Martín, Sergio
Faulin Fajardo, Francisco Javier
|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|
|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.|
|Appears in Collections:||Articles|
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