A modified multi-agent proximal policy optimization algorithm for multi-objective dynamic partial-re-entrant hybrid flow shop scheduling problem

This paper extends a novel model for modern flexible manufacturing systems: the multi-objective dynamic partial-re-entrant hybrid flow shop scheduling problem (MDPR-HFSP). The model considers partial-re-entrant processing, dynamic disturbance events, green manufacturing demand, and machine workload....

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Veröffentlicht in:	Engineering applications of artificial intelligence Jg. 140; S. 109688
Hauptverfasser:	Wu, Jiawei, Liu, Yong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 15.01.2025
Schlagworte:	Deep reinforcement learning Dynamic scheduling Multi-agent Multi-objective optimization Partial-re-entrant hybrid flow shop Proximal policy optimization Multi-agent Dynamic scheduling Deep reinforcement learning Proximal policy optimization Multi-objective optimization Partial-re-entrant hybrid flow shop
ISSN:	0952-1976
Online-Zugang:	Volltext
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