Forthcoming articles

International Journal of Experimental Design and Process Optimisation

International Journal of Experimental Design and Process Optimisation (IJEDPO)

These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Register for our alerting service, which notifies you by email when new issues are published online.

Open AccessArticles marked with this Open Access icon are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.
We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Experimental Design and Process Optimisation (1 paper in press)

Regular Issues

  • USING CRITERION-BASED MODEL AVERAGING IN TWO-INPUT MRSM PROBLEMS: Investigating Cloning of an Under-Fitted Response Model   Order a copy of this article
    by Domingo Pavolo, Delson Chikobvu 
    Abstract: Cloning of an under-fitted parent ordinary least squares response model using model averaging to combine its genetic ordinary least squares models is presented and investigated as a solution to the models problem of parametric bias and variability with the intention of improving prediction accuracy. The permutation of genetic models that is produced from the set of the under-fitted ordinary least squares models independent variables is first determined. Sets of genetic models that combine to give the same functional form as the parent ordinary least squares model are then obtained and combined using criterion-based model averaging. The mean squared error of the clones are very close to that of the parent ordinary least squares model but always larger. The mean squared forecasted error suggest that most of the clones have better prediction accuracy than the ordinary least squares model. Combining the clones using criterion-based frequentist model averaging and arithmetic model averaging shows that the higher the number of clones combined using criterion-based frequentist model averaging the better the fitness to data than arithmetic model averaging while the higher the number of clones combined using arithmetic averaging the better the prediction accuracy.
    Keywords: response surface cloning; multi-response surface methodology; ordinary least squares model; response models; all regressions modeling.