Forecasting container throughputs at ports using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Chen20102054,
  author =       "Shih-Huang Chen and Jun-Nan Chen",
  title =        "Forecasting container throughputs at ports using
                 genetic programming",
  journal =      "Expert Systems with Applications",
  volume =       "37",
  number =       "3",
  pages =        "2054--2058",
  year =         "2010",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2009.06.054",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-4WNXTWY-M/2/1a5e0fe084ba3ea36303bd280acecc04",
  keywords =     "genetic algorithms, genetic programming, Container
                 throughput, Forecasting",
  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

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