Titlebook: Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling; Schirin Bär Book 2022 The Editor(s) (if applicable)

爱好

Ernesto Noronha,Premilla D‘Cruzal of customer requirements of (Huang et al., 2011). The customer requirements were collected and weighted in the first step in chapter .. Through the conceptual design schemes, the customer requirements were considered and embodied in chapter ..

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consolidate

观察

雇佣兵

peak-flow

Bother

Critical Discussion and Outlook,tunities for further research, thereby performing step ten of the HoQ. In discussion rounds with researchers and experts, we analyzed the results to identify aspects that were not yet considered in the concept because they were not listed in the requirements, but which could become requirements in the future.

PALL

Summary,production control that should be flexible, e.g., in terms of available machines, new product variants, and unforeseen events. With the contribution of a reactive scheduling system and the integration concept for existing FMSs, we successfully achieved the determined goals defined by the three research questions.

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https://doi.org/10.1007/978-3-658-39179-9Production Scheduling; Flexible Manufacturing; Machine Learning; Multi-Agent System; Reinforcement Learn

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978-3-658-39178-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wies