This paper proposes a flexible solution methodology for solving the Vehicle Routing Problem with Stochastic Demands (VRPSD). The logic behind this methodology is to transform the issue of solving a given VRPSD instance into an issue of solving a small set of Capacitated Vehicle Routing Problem (CVRP) instances. Thus, our approach takes advantage of the fact that extremely efficient metaheuristics for the CVRP already exists. The CVRP instances are obtained from the original VRPSD instance by assigning different values to the level of safety stocks that routed vehicles must employ to deal with unexpected demands. The methodology also makes use of Monte Carlo Simulation (MCS) to obtain estimates of the expected costs associated with corrective routing actions (recourse actions) after a vehicle runs out of load before completing its route. ; Postprint (published version)


    Access

    Download


    Export, share and cite



    Title :

    A simulation-based algorithm for solving the Vehicle Routing Problem with Stochastic Demands



    Publication date :

    2011-01-01


    Type of media :

    Miscellaneous


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




    A Tabu Search Algorithm for the Vehicle Routing Problem with Stochastic Demands

    Liu, Xing / Shen, Changli / Wang, Guiqiang | ASCE | 2008


    [257] A MEMETIC ALGORITHM FOR THE VEHICLE ROUTING PROBLEM WITH STOCHASTIC DEMANDS

    Gutierrez, Andres / Dieulle, Laurence / Labadie, Nacima et al. | TIBKAT | 2016


    SIM-Randsharp: A hybrid algorithm for solving the ARC routing problem with stochastic demands

    González, Sergio / Riera, Daniel / Juan, Angel A. et al. | BASE | 2012

    Free access

    An Effective Tabu Search Algorithm for the Vehicle Routing Problem with Stochastic Demands

    Hu, Shan Liang ;Liu, Chang Shi | Trans Tech Publications | 2011


    A Genetic Algorithm in Combination with a Solution Archive for Solving the Generalized Vehicle Routing Problem with Stochastic Demands

    Biesinger, Benjamin / Hu, Bin / Raidl, Günther R. | British Library Online Contents | 2018