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The Gene Expression Programming Applied to Demand Forecast

  • Evandro Bittencourt
  • Sidney Schossland
  • Raul Landmann
  • Dênio Murilo de Aguiar
  • Adilson Gomes De Oliveira
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 73)

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.

Keywords

Genetic Algorithm Genetic Program Initial Population Supply Chain Management Gene Expression Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Evandro Bittencourt
    • 1
  • Sidney Schossland
    • 1
  • Raul Landmann
    • 1
  • Dênio Murilo de Aguiar
    • 1
  • Adilson Gomes De Oliveira
    • 1
  1. 1.Department of ManagementUNIVILLEJoinvilleBrazil

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