Combining biased randomization with meta-heuristics for solving the multi-depot vehicle routing problem

ANGEL A. JUAN; MARIANA CÓCCOLA; JAVIER FAULIN; BARRY BARRIOS; TOLGA BEKTAS; SERGIO GONZALEZ-MARTIN

Berlín

Conferencia; Winter Simulation Conference, WSC 2012; 2012

C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A.M. Uhrmacher

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.