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Dual deep reinforcement learning agents-based integrated order acceptance and scheduling of mass individualized prototyping.
- Source :
-
Journal of Cleaner Production . Nov2023, Vol. 427, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09596526
- Volume :
- 427
- Database :
- Academic Search Index
- Journal :
- Journal of Cleaner Production
- Publication Type :
- Academic Journal
- Accession number :
- 173234642
- Full Text :
- https://doi.org/10.1016/j.jclepro.2023.139249