Combining biased randomization with iterated local search for solving the multidepot vehicle routing problem

Angel A. Juan, Iñaki Pascual, Daniel Guimarans, Barry Barrios
2014 International Transactions in Operational Research  
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
more » ... 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.
doi:10.1111/itor.12101 fatcat:4bx2ctbxmfhvtjy3qwzgnwlawm