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- W3147257862 abstract "Computer simulations of manufacturing processes are in widespread use for optimizing production planning and order processing. If unforeseeable events are common, real-time decisions are necessary to maximize the performance of the manufacturing process. Pre-trained AI-based decision support offers promising opportunities for such time-critical production processes. Here, we explore the effectiveness of deep reinforcement learning for real-time decision making in a car manufacturing process. We combine a simulation model of a central production part, the line buffer, with deep reinforcement learning algorithms, in particular with deep Q-Learning and Monte Carlo tree search. We simulate two different versions of the buffer, a single-agent and a multi-agent one, to generate large amounts of data and train neural networks to represent near-optimal strategies. Our results show that deep reinforcement learning performs extremely well and the resulting strategies provide near-optimal decisions in real-time, while alternative approaches are either slow or give strategies of poor quality." @default.
- W3147257862 created "2021-04-13" @default.
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- W3147257862 date "2020-12-14" @default.
- W3147257862 modified "2023-09-24" @default.
- W3147257862 title "Real-Time Decision Making for a Car Manufacturing Process Using Deep Reinforcement Learning" @default.
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- W3147257862 doi "https://doi.org/10.1109/wsc48552.2020.9383884" @default.
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