IDEAS home Printed from https://ideas.repec.org/a/ids/ijisen/v11y2012i3p205-224.html
   My bibliography  Save this article

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

Author

Listed:
  • Ali Azadeh
  • Maryam Mirjalili
  • Mohammad Sheikhalishahi
  • Shima Nassiri

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.

Suggested Citation

  • Ali Azadeh & Maryam Mirjalili & Mohammad Sheikhalishahi & Shima Nassiri, 2012. "An integrated genetic algorithm-conventional regression-analysis of variance for improvement of gasoline demand estimation," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 11(3), pages 205-224.
    • Handle: RePEc:ids:ijisen:v:11:y:2012:i:3:p:205-224
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=47096
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijisen:v:11:y:2012:i:3:p:205-224. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=188 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.