Abstract
This paper examines the use of artificial intelligence (in particular the aplication of Gene Expression Programming, GEP) to demand forecasting. 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. 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 others quantitatives method. Thus, from a data set of about demand of consumption of twelve products line metal fittings, we have compared the forecast data.
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Bittencourt, E., Schossland, S., Landmann, R., de Aguiar, D.M., De Oliveira, A.G. (2010). The Gene Expression Programming Applied to Demand Forecast. In: Corchado, E., Novais, P., Analide, C., Sedano, J. (eds) Soft Computing Models in Industrial and Environmental Applications, 5th International Workshop (SOCO 2010). Advances in Intelligent and Soft Computing, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13161-5_25
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DOI: https://doi.org/10.1007/978-3-642-13161-5_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13160-8
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