This study leverages simulation-optimisation with a Reinforcement Learning (RL) model to analyse the routing behaviour of delivery vehicles (DVs). We conceptualise the system as a stochastic k-armed bandit problem, representing a sequential interaction between a learner (the DV) and its surrounding environment. Each DV is assigned a random number of customers and an initial delivery route. If a loading zone is unavailable, the RL model is used to select a delivery strategy, thereby modifying its route accordingly. The penalty is gauged by the additional trucking and walking time incurred compared to the originally planned route. Our methodology is tested on a simulated network featuring realistic traffic conditions and a fleet of DVs employing four distinct lastmile delivery strategies. The results of our numerical experiments underscore the advantages of providing DVs with an RL-based decision support system for en-route decision-making, yielding benefits to the overall efficiency of the transport network.

    Highlights

    Combining simulation and optimisation algorithms with reinforcement learning

    Model DVs en-route parking decisions with a k-armed bandit algorithm

    Evaluating the impacts of delivery strategies on traffic congestion and in last-mile delivery efficiency


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A reinforcement learning framework for improving parking decisions in last-mile delivery


    Weitere Titelangaben:

    TRANSPORTMETRICA B: TRANSPORT DYNAMICS
    J. E. MURIEL ET AL.


    Beteiligte:
    Muriel, Juan E. (Autor:in) / Zhang, Lele (Autor:in) / Fransoo, Jan C. (Autor:in) / Villegas, Juan G. (Autor:in)


    Erscheinungsdatum :

    31.12.2024


    Format / Umfang :

    29 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Deep reinforcement learning for stochastic last-mile delivery with crowdshipping

    Marco Silva / João Pedro Pedroso / Ana Viana | DOAJ | 2023

    Freier Zugriff

    Parking Guidance and Geofencing for Last-Mile Delivery Operations

    Simoni, Michele D. | IEEE | 2024

    Freier Zugriff

    Last mile delivery system

    SALTER STUART C / WAAG ANDR ¨ / DAVID BRIAN GLICKMAN et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    LAST MILE DELIVERY SYSTEM

    SALTER STUART C / WAGH ARNAV / GLICKMAN DAVID BRIAN et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    Comparing Reinforcement Learning Algorithms for Online Couriers Scheduling in Crowdsourced Last-Mile Delivery

    Saleh, Zead / Baubaid, Ahmad / Al Hanbali, Ahmad et al. | Elsevier | 2025

    Freier Zugriff