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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Vydáno v:Neural computing & applications Ročník 28; číslo 11; s. 3163 - 3182
Hlavní autoři: Li, Ming-Wei, Hong, Wei-Chiang, Geng, Jing, Wang, Jianlun
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.11.2017
Springer Nature B.V
Témata:
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
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Shrnutí: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 and to provide efficient service. In this paper, we propose a multi-objective berth and QC coordinated scheduling model, namely M-B&QC, by taking the minimum additional trucking distance and the port time of ships as the optimization objectives, with the constraints based on demand of port operations and vessel berthing. To solve the M-B&QC model, the particle coding rule and the particle feasible-integer processing module (namely PF-IP) for improving PSO performance are employed to determine the computation strategies of individual historical optimal value p i G and global optimal value P g G of particle for the multi-objective optimization. In addition, the global disturbance with cat mapping function (namely GDCM) and local search with cloud model (namely LSCM) are also hybridized, namely PM&CCPSO algorithm, to solve the M-B&QC model. Numerical experiments including eight combined examples are conducted to test the performance of the proposed programming model and the modified solving algorithm.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-016-2226-7