دانلود مقاله انگلیسی رایگان:زمانبندی مبتنی بر یادگیری تقویتی عمیق مبتنی بر AGV با قاعده مختلط برای کف انعطاف پذیر در صنعت 4.0 - 2020
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  • Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0 Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0
    Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0


    ترجمه فارسی عنوان مقاله:

    زمانبندی مبتنی بر یادگیری تقویتی عمیق مبتنی بر AGV با قاعده مختلط برای کف انعطاف پذیر در صنعت 4.0


    منبع:

    Sciencedirect - Elsevier - Computers & Industrial Engineering, 149 (2020) 106749. doi:10.1016/j.cie.2020.106749


    نویسنده:

    Hao Hu , Xiaoliang Jia *, Qixuan He , Shifeng Fu , Kuo Liu


    چکیده انگلیسی:

    Driven by the recent advances in industry 4.0 and industrial artificial intelligence, Automated Guided Vehicles (AGVs) has been widely used in flexible shop floor for material handling. However, great challenges aroused by the high dynamics, complexity, and uncertainty of the shop floor environment still exists on AGVs real-time scheduling. To address these challenges, an adaptive deep reinforcement learning (DRL) based AGVs real-time scheduling approach with mixed rule is proposed to the flexible shop floor to minimize the makespan and delay ratio. Firstly, the problem of AGVs real-time scheduling is formulated as a Markov Decision Process (MDP) in which state representation, action representation, reward function, and optimal mixed rule policy, are described in detail. Then a novel deep q-network (DQN) method is further developed to achieve the optimal mixed rule policy with which the suitable dispatching rules and AGVs can be selected to execute the scheduling towards various states. Finally, the case study based on a real-world flexible shop floor is illustrated and the results validate the feasibility and effectiveness of the proposed approach.
    Keywords: Automated guided vehicles | Real-time scheduling | Deep reinforcement learning | Industry 4.0


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 9
    حجم فایل: 2738 کیلوبایت

    قیمت: رایگان


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