Manufacturing processes of assembly lines are now changing from a single factory to distributed multi-factories for minimizing costs and meeting different customer demands. To achieve the production capacity of the distributed manufacturing system, minimize costs incurred during production as well as deal with the influence of traffic conditions on inter-factory material handling, the production rates of machines at each production stage need to be adjusted in a coordinated manner. With the emerging Industry 4.0 technologies, reinforcement learning techniques can be used for optimizing resources usage during production. To achieve better coordination, a multi-agent deep reinforcement learning (MADRL) based framework is proposed to deal with the distributed manufacturing resources optimization problem. A multi-agent PPO based algorithm MAPPO is developed in this framework and evaluated by comparing with the MADDPG and DDQN algorithms. Our simulation results show that MAPPO is able to achieve the production capacity with lower energy, storage, computational and communication costs.


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    Title :

    Multi-Agent Deep Reinforcement Learning based Multi-Objective Resource Optimization in a Distributed Manufacturing System


    Contributors:


    Publication date :

    2024-06-24


    Size :

    1265048 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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