Pickup and delivery problems with late penalties can be adopted to model a wide range of practical situations in the field of transportation and logistics. However, the restrictions on the multiple vehicles’ service sequences and non-linearity caused by the late penalties make it time-consuming to solve this problem. To overcome this difficulty, we propose a novel reinforcement learning framework inspired by transformer architecture to generate tours instantly after offline training. This framework, as trained through the policy gradient method, consists of the information encoder process which can extract the coupling relationships among the pickup and delivery customers, and the decoder process with multi-vehicle attention network to allocate reasonable orders to each vehicle. Validated on Sioux Falls network, the proposed method yields the improvement of 2.4%-8.0% on the solution quality compared with Google OR-Tools and several heuristic algorithms. Notably, the baselines require dozens of minutes to achieve a lesser result on the case with 100 customers while the well-trained model based on our method can be deployed to provide a high-quality solution within seconds. Furthermore, the proposed model also shows good generalization ability in different scenarios with various scale problems, and the obtained results are shown to be quite robust to counter the fluctuation of travel time.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Transformer-Based Reinforcement Learning for Pickup and Delivery Problems With Late Penalties


    Beteiligte:
    Zhang, Ke (Autor:in) / Lin, Xi (Autor:in) / Li, Meng (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-12-01


    Format / Umfang :

    4587572 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Automated reinforcement learning scenario variation and impact penalties

    LINSCOTT GARY / PASTERNAK ANDREAS / PACKER JEFFERSON BRADFIELD et al. | Europäisches Patentamt | 2024

    Freier Zugriff


    A survey on pickup and delivery problems. Part II: Transportation between pickup and delivery locations

    Parragh, Soophie N. / Doerner, Karl F. / Hartl, Richard F. | Tema Archiv | 2008



    Real-Time Multivehicle Truckload Pickup and Delivery Problems

    Yang, J. / Jaillet, P. / Mahmassani, H. | British Library Online Contents | 2004