Deep learning-enhanced quantum optimization for integrated job scheduling in container terminals

This study explored the challenge of multi-equipment collaborative scheduling and management within container terminals, focusing on essential equipment such as reach stackers, automated guided vehicles (AGVs), quay cranes, and yard cranes. The job shop scheduling problem (JSSP) in container handlin...

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Veröffentlicht in:	Engineering applications of artificial intelligence Jg. 148; S. 110431
Hauptverfasser:	Cuong, Truong Ngoc, Kim, Hwan-Seong, Bao Long, Le Ngoc, You, Sam-Sang, Tan, Nguyen Duy
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 15.05.2025
Schlagworte:	Container terminal Deep learning Job scheduling Multiple equipment Quantum optimization algorithm Deep learning Multiple equipment Container terminal Job scheduling Quantum optimization algorithm
ISSN:	0952-1976
Online-Zugang:	Volltext
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