Highlights A novel reinforcement learning algorithm that can solve vehicle routing problem with soft time window. An encoder-decoder framework with attention layers to generate tours of multiple vehicles iteratively. Proposed method outperforms benchmark models and Google OR-Tools in most scenarios with much little computation time. The pre-trained model can be deployed instantly without further training for new problems.

    Abstract Multi-vehicle routing problem with soft time windows (MVRPSTW) is an indispensable constituent in urban logistics distribution systems. Over the past decade, numerous methods for MVRPSTW have been proposed, but most are based on heuristic rules that require a large amount of computation time. With the current rapid increase of logistics demands, traditional methods incur the dilemma between computational efficiency and solution quality. To efficiently solve the problem, we propose a novel reinforcement learning algorithm called the Multi-Agent Attention Model that can solve routing problem instantly benefit from lengthy offline training. Specifically, the vehicle routing problem is regarded as a vehicle tour generation process, and an encoder-decoder framework with attention layers is proposed to generate tours of multiple vehicles iteratively. Furthermore, a multi-agent reinforcement learning method with an unsupervised auxiliary network is developed for the model training. By evaluated on four synthetic networks with different scales, the results demonstrate that the proposed method consistently outperforms Google OR-Tools and traditional methods with little computation time. In addition, we validate the robustness of the well-trained model by varying the number of customers and the capacities of vehicles.


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    Titel :

    Multi-vehicle routing problems with soft time windows: A multi-agent reinforcement learning approach


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


    Erscheinungsdatum :

    2020-10-27




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch