Combining biased randomization with meta-heuristics for solving the multi-depot vehicle routing problem
Year: 2012
Type: conference-paper
Abstract: This paper proposes a hybrid algorithm, combining Biased-Randomized (BR) processes with an Iterated\nLocal Search (ILS) meta-heuristic, to solve the Multi-Depot Vehicle Routing Problem (MDVRP).\nOur approach assumes a scenario in which each depot has unlimited service capacity and in which all vehicles\nare identical (homogeneous fleet). During the routing process, however, each vehicle is assumed to\nhave a limited capacity. Two BR processes are employed at different stages of the ILS procedure in order\nto: (a) define the perturbation operator, which generates new ‘assignment maps’ by associating customers\nto depots in a biased-random way –according to a distance-based criterion; and (b) generate ‘good’ routing\nsolutions for each customers-depots assignment map. These biased-randomization processes rely on\nthe use of a pseudo-geometric probability distribution. Our approach does not need from fine-tuning processes\nwhich usually are complex and time consuming. Some preliminary tests have been carried out already\nwith encouraging results.
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2153289685