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- W3147777387 abstract "Steel plates are supplied to shipyards without adhering to the processing schedule because they are ordered in bulk according to the steel market condition and the mid- to long-term production strategy. Hence, steel plates are stacked up in the steel stock yard until they are input to the processing system and then supplied sequentially according to the processing start date. Currently, a steel stock yard, is operated by the experience of field workers, and inefficiencies such as excessive crane use are occurring, so efficient management techniques are required. However, the conventional optimization algorithm has limitations because the input timing of the steel is random. In this study, a study was conducted to determine the order of steel input into steel stock yard using reinforcement learning algorithm. Effective algorithm(A3C) can be identified through tests, and it was validated that proposed method is effective for problems of actual size steel stock yard." @default.
- W3147777387 created "2021-04-13" @default.
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- W3147777387 date "2020-12-14" @default.
- W3147777387 modified "2023-09-26" @default.
- W3147777387 title "Machine Learning (Reinforcement Learning)-Based Steel Stock Yard Planning Algorithm" @default.
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- W3147777387 doi "https://doi.org/10.1109/wsc48552.2020.9384049" @default.
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