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Title:
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THE GENE EXPRESSION PROGRAMMING APPLIED TO THE SEASONAL DEMAND FORECAST |
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Author(s):
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Evandro Bittencourt , Raul Landmann , Paulo César Oliveira , Sidney Schossland , Edson Wilson Torrens , Jerzy Wyrebski |
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ISBN:
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978-972-8924-87-4 |
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Editors:
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António Palma dos Reis |
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Year:
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2009 |
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Edition:
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Single |
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Keywords:
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Demand forecast, gene expression programming, seasonality. |
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Type:
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Full Paper |
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First Page:
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27 |
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Last Page:
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34 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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This paper addresses the application of the Gene Expression Programming (GEP), an Artificial Intelligence technique,
applied in demand forecasting applied for the Production Management. In the world of production management, many
data that are produced in function of the of economic activity characteristics in which they belong, may suffer, for
example, significant impacts of seasonal behaviors, making the prediction of future conditions difficult by means of
methods commonly used. Thus, the search for mathematical functions that fit the varied conditions, such as those
observed in the demand for some products can not be carried out with simple numerical methods and functions. The GEP
is an evolution of Genetic Programming, which is part of the Genetic Algorithms. GEP seeks for mathematical functions,
adjusting to a given set of solutions using a type of genetic heuristics from a population of random functions. In order to
compare the GEP, we have used the method of seasonal indices. Thus, from a data set of about consumption of barrels of
gasoline for 12 months, we have compared the forecast data. The results showed that GEP can be used as a technique in
forecasting, and is better than the seasonal indices method. |
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