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




Developing a short-term comparative optimization forecasting model for operational units’ strategic planning - Download this document for free, or read online. Document in PDF available to download.

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 -

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

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 MethodsC - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: GeneralC - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics-





Author: Filippou, Miltiades

Source: https://mpra.ub.uni-muenchen.de/30766/







Related documents