DocumentCode :
169130
Title :
An artificial intelligence approach to calculate the capacity of bulk cargo terminal
Author :
Bingli Yan ; Qiang Zhou
Author_Institution :
Sch. of Logistics Eng., Wuhan Univ. of Technol., Wuhan, China
fYear :
2014
fDate :
21-23 May 2014
Firstpage :
1
Lastpage :
5
Abstract :
In this paper, we introduced an artificial intelligence approach to analyze and calculate the capacity of bulk cargo terminal. It included two parts, the artificial neural network model and the genetic algorithm model. The artificial neural network model was used to analyze the weights between throughput of bulk cargo terminal and impact elements in bulk cargo terminal, and the genetic algorithm model was used to calculate the capacity of bulk cargo terminal. The data for this paper was gained from the authority of a coal terminal in China. An artificial neural network model with 9 input neurons and 1 output neuron was built and to analyze the operation of the coal terminal. According to analysis and calculation, the artificial neural network model was valid, because all errors between the outputs of the artificial neural network model and the outputs in real were less than 0.4%. A genetic algorithm model was designed to calculate the capacity of this coal terminal, which was 8559684.216121702 tons/month.
Keywords :
artificial intelligence; coal; genetic algorithms; goods dispatch data processing; neural nets; sea ports; China; artificial neural network model; bulk cargo terminal capacity; coal terminal; genetic algorithm model; impact elements; input neurons; output neuron; Collaborative work; Conferences; Decision support systems; Handheld computers; Manganese; TV; artificial intelligence; artificial neural network; bulk cargo terminal; capacity; genetic algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Supported Cooperative Work in Design (CSCWD), Proceedings of the 2014 IEEE 18th International Conference on
Conference_Location :
Hsinchu
Type :
conf
DOI :
10.1109/CSCWD.2014.6846807
Filename :
6846807
Link To Document :
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