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Developing a short-term comparative optimization forecasting model for operational units’ strategic planning

Author

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  • Filippou, Miltiades
  • Zervopoulos, Panagiotis

Abstract

Data drain for peer active units operating in the same sector is a major factor that prevents policy makers from developing flawless strategic plans for their organisation. This study introduces a hybrid model that incorporates a purely deterministic method, Data Envelopment Analysis (DEA), and a semi-parametric technique, Artificial Neural Networks (ANNs), to provide a strategic planning tool for efficiency optimization applicable to short-term lag of data availability. For consecutive time instances, t and t+1, the developed DEANN model returns optimum “regression-type” input and output levels for every sample operational unit, even for the fully efficient ones, that may decide to alter the levels of the efficiency determinants, respecting the t-time efficiency frontier.

Suggested Citation

  • Filippou, Miltiades & Zervopoulos, Panagiotis, 2011. "Developing a short-term comparative optimization forecasting model for operational units’ strategic planning," MPRA Paper 30766, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:30766
    as

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    File URL: https://mpra.ub.uni-muenchen.de/30766/1/MPRA_paper_30766.pdf
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    References listed on IDEAS

    as
    1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    2. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Forecasting; Optimization; Efficiency; Data Envelopment Analysis (DEA); Artificial Neural Networks (ANN); Adaptive Techniques;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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