Forecasting container throughputs at ports using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Chen20102054,
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author = "Shih-Huang Chen and Jun-Nan Chen",
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title = "Forecasting container throughputs at ports using
genetic programming",
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journal = "Expert Systems with Applications",
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volume = "37",
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number = "3",
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pages = "2054--2058",
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year = "2010",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2009.06.054",
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URL = "http://www.sciencedirect.com/science/article/B6V03-4WNXTWY-M/2/1a5e0fe084ba3ea36303bd280acecc04",
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keywords = "genetic algorithms, genetic programming, Container
throughput, Forecasting",
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abstract = "To accurately forecast container throughput is crucial
to the success of any port operation policy. This study
attempts to create an optimal predictive model of
volumes of container throughput at ports by using
genetic programming (GP), decomposition approach
(X-11), and seasonal auto regression integrated moving
average (SARIMA). Twenty-nine years of historical data
from Taiwan's major ports were collected to establish
and validate a forecasting model. The Mean Absolute
Percent Error levels between forecast and actual data
were within 4percent for all three approaches. The GP
model predictions were about 32-36percent better than
those of X-11 and SARIMA. These results suggest that GP
is the optimal method for this case. GP predicted that
container through puts at Taiwan's major ports would
slowly increase in the year 2008. Since Taiwan's
government opened direct transportation with China in
July 2008, the issue of container throughput in Taiwan
has become even more worthy of discussion.",
- }
Genetic Programming entries for
Shih-Huang Chen
Junn-nan Chen
Citations