This paper discusses the use of deep reinforcement learning to resolve deadlocks in material flow systems with automated guided vehicles (AGVs). The paper proposes a strategy for dealing with deadlocks based on a single Agent reinforcement learning approach (SARL). The agent will find the optimal solution strategy in real time. The proposed approach is evaluated using a material flow simulation for a real use case in industry. The effectiveness in reducing the occurrence of deadlocks as well as the number of collisions in the system is demonstrated. This study highlights the potential of deep reinforcement learning for improving the performance and efficiency of material flow systems with AGVs.


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    Title :

    Simulation-based resolution of deadlocks in automated guided vehicles using deep reinforcement learning ; Simulationsgestützte Lösung von Deadlocks bei fahrerlosen Transportsystemen mit Hilfe von Deep Reinforcement Learning


    Contributors:

    Publication date :

    2023-09-13



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629 / 650



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