Crowdshipping problem can be challenging as the platform are continuously but sporadically receiving crowdshippers and delivery tasks with heterogeneous origin and destination. In this paper, the dynamic multi-vehicle pickup and delivery problem with crowdshippers (DMV-PDPC) is considered. Leveraging the deep reinforcement learning framework, the attention model with centralized vehicle network (AMCVN) method is developed. Unlike traditional heuristic or existing vehicle-changing methods, AMCVN integrates a centralized vehicle network (CVN) that can observe the state information of all vehicles, enhancing its overall performance. In each decision-making step, the CVN monitors the state of the vehicles and selects one of the vehicles. Subsequently, the attention-based route generating network (RGN) determines the next node to be visited by the chosen vehicle. Instead of using a penalty term in the reward function to regulate the sequence of visits to pickup and delivery nodes, a more precise control method, namely the rolling mask scheme (RMS), is implemented. The method’s evaluation is carried out via a simulation experiment using a real-world road network. This evaluation demonstrates that the proposed method effectively tackles the DMV-PDPC challenge, outperforming current state-of-the-art learning-based models and heuristic methods. Moreover, the method shows exceptional generalization capabilities, as evidenced by its adaptability to different numbers of tasks and vehicles.


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    Titel :

    Centralized Deep Reinforcement Learning Method for Dynamic Multi-Vehicle Pickup and Delivery Problem With Crowdshippers


    Beteiligte:
    Xiang, Chuankai (Autor:in) / Wu, Zhibin (Autor:in) / Tu, Jiancheng (Autor:in) / Huang, Jun (Autor:in)


    Erscheinungsdatum :

    01.08.2024


    Format / Umfang :

    2598778 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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