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

Filippou, Miltiades and Zervopoulos, Panagiotis (2011): Developing a short-term comparative optimization forecasting model for operational units’ strategic planning.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Forecasting, Optimization, Efficiency, Data Envelopment Analysis (DEA), Artificial Neural Networks (ANN), Adaptive Techniques
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics

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