Berth and quay crane coordinated scheduling using multi-objective chaos cloud particle swarm optimization algorithm

The demand for the maritime transportation has significantly increased over the past 20 years due to the rapid pace of globalization. Terminal managers confront the challenge in establishing the appropriate berth and quay crane (QC) coordinated schedule to achieve the earliest departure time of ship...

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Veröffentlicht in:	Neural computing & applications Jg. 28; H. 11; S. 3163 - 3182
Hauptverfasser:	Li, Ming-Wei, Hong, Wei-Chiang, Geng, Jing, Wang, Jianlun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.11.2017 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Berthing Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Freight transportation Globalization Image Processing and Computer Vision Marine transportation Mathematical models Multiple objective analysis Original Article Particle swarm optimization Probability and Statistics in Computer Science Scheduling Ships Multi-objective optimization Container terminal Cloud model Chaos theory Berth and QC scheduling Particle swarm optimization
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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