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

Miltiades Filippou and Panagiotis Zervopoulos

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Forecasting; Optimization; Efficiency; Data Envelopment Analysis (DEA); Artificial Neural Networks (ANN); Adaptive Techniques (search for similar items in EconPapers)
JEL-codes: C14 C45 C53 (search for similar items in EconPapers)
Date: 2011-04-20
New Economics Papers: this item is included in nep-eff and nep-for
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