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. ; Postprint (published version)


    Access

    Download


    Export, share and cite



    Title :

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



    Publication date :

    2012-01-01


    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629 / 000




    On Solving the Multi-depot Vehicle Routing Problem

    Tlili, Takwa / Krichen, Saoussen / Drira, Ghofrane et al. | Springer Verlag | 2015


    Using biased randomization for solving the two-dimensional loading vehicle routing problem with heterogeneous fleet

    Dominguez, Oscar / Juan, Angel A. / Barrios, Barry et al. | Springer Verlag | 2014



    Multi-Type Attention for Solving Multi-Depot Vehicle Routing Problems

    Li, Jinqi / Tian Dai, Bing / Niu, Yunyun et al. | IEEE | 2024


    Multi-Depot Vehicle Routing Problem with Hybrid Genetic Algorithm

    Dang, Li Wei ;Sun, Xiao Ming | Trans Tech Publications | 2012