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- W4387490877 abstract "Coordinating order acceptance decisions with production scheduling to maximize revenue is challenging for Mass Individualized Prototyping (MIP) service providers. This paper presents a dual deep reinforcement learning agents-based (DDRLA) integrated order acceptance and scheduling (IOAS) for improving revenue. Firstly, a deep reinforcement learning-based virtual production scheduling (VPS) agent together with 8 state features and 11 action rules is designed. The VPS agent quickly and virtually reschedules a dynamically-arriving accepted order to evaluate the overall impact of accepting this order, including consumed capacity and increased revenue. Then, a deep reinforcement learning-based order acceptance decision (OAD) agent is designed. Based on the information guidance resulting from an interaction with the VPS agent, the OAD agent selectively accepts orders to maximize long-term gains, as well as to improve system resilience in the presence of a high ratio of urgent orders. The experiment results show that the proposed DDRLA method has better performance, compared with other IOAS approaches." @default.
- W4387490877 created "2023-10-11" @default.
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- W4387490877 date "2023-11-01" @default.
- W4387490877 modified "2023-10-15" @default.
- W4387490877 title "Dual deep reinforcement learning agents-based integrated order acceptance and scheduling of mass individualized prototyping" @default.
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- W4387490877 doi "https://doi.org/10.1016/j.jclepro.2023.139249" @default.
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