This paper focuses on a deep reinforcement learning (DRL)-based approach for Heterogeneous Vehicle Routing Problem, where each vehicle in the fleet is characterized by its capacity and speed. Previous methods fail to optimize the route for a vehicle while cooperatively considering the current state (e.g., the remaining capacities and the positions) of other vehicles in the fleet. To solve this problem, we first propose a chronological Markov Decision Process, which makes the current state of the whole fleet available. Second, we propose a fleet encoder, a specific network architecture to promote cooperative route generation among vehicles by incorporating the fleet state. Experimental results show that our method outperforms other DRL-based methods on problems with any number of customers and vehicles.


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    Titel :

    A Chronological and Cooperative Route Optimization Method for Heterogeneous Vehicle Routing Problem


    Beteiligte:
    Yamaguchi, Yosuke (Autor:in) / Wang, Zhao (Autor:in) / Nakano, Yuusuke (Autor:in) / Ohya, Jun (Autor:in) / Hasegawa, Katsuya (Autor:in)


    Erscheinungsdatum :

    24.07.2023


    Format / Umfang :

    433736 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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