Int. J. of Industrial and Systems Engineering   »   2012 Vol.11, No.3

 

 

Title: An integrated genetic algorithm-conventional regression-analysis of variance for improvement of gasoline demand estimation

 

Authors: Ali Azadeh; Maryam Mirjalili; Mohammad Sheikhalishahi; Shima Nassiri

 

Addresses:
Department of Industrial Engineering, Center of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran, Tehran, Iran
Department of Industrial Engineering, Center of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran, Tehran, Iran
Department of Industrial Engineering, Center of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran, Tehran, Iran
Department of Industrial Engineering, Center of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran, Tehran, Iran

 

Abstract: This study presents an integrated algorithm for forecasting gasoline demand based on genetic algorithm (GA) with variable parameters using stochastic procedures, conventional regression and analysis of variance (ANOVA). The proposed algorithm uses ANOVA to select either GA or conventional regression for future demand estimation. It uses minimum absolute percentage of error (MAPE) when the null hypothesis in ANOVA is accepted to select from GA or regression model. The significance of the proposed algorithm is twofold. Firstly, it is flexible and identifies the best model based on the results of ANOVA and MAPE. Secondly, the proposed algorithm may identify conventional regression as the best model for future gasoline demand forecasting because of its dynamic structure, whereas previous studies assume that GA always provide the best solutions and estimation. To show the applicability and superiority of the proposed algorithm, the data for gasoline demand in Iranian agriculture sector from 1972 to 2002 is used and applied to the proposed algorithm.

 

Keywords: integration; GAs; genetic algorithms; gasoline demand; ANOVA; analysis of variance; conventional regression; demand estimation; petrol; demand forecasting; Iran; agriculture sector.

 

DOI: 10.1504/IJISE.2012.047096

 

Int. J. of Industrial and Systems Engineering, 2012 Vol.11, No.3, pp.205 - 224

 

Available online: 28 May 2012

 

 

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