Filippou, Miltiades and Zervopoulos, Panagiotis (2011): Developing a hybrid comparative optimization model for shortterm forecasting: an ‘idle time interval’ roadmap for operational units’ strategic planning.

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Abstract
Data drain and data uncertainties for rival units affect the reliability and effectiveness of strategic plans for individual operational units. This study introduces a stochastic, multistage, optimization technique for shortterm forecasting that intends to assist policy makers in developing ‘flawless’ plans for their organizations during the idle time interval in which official data and balancesheet reports of the competitors are unavailable. The developed technique, called SDEANN, draws on the ‘deterministic’ data envelopment analysis (DEA) method, ‘regressiontype’ artificial neural networks (ANNs), and the contamination of the outputs of the DEA analysis with statistical noise. Statistical noise represents the bias of a ‘deterministic’ sample optimum production frontier when generalization or the uncertainty of the data used becomes the issue. The SDEANN model respects the monotonicity assumption that prevails in microeconomic theory, uses the DEA definition of efficiency, and addresses the dimensionality issues of ANNs with minimum sample size requirements.
Item Type:  MPRA Paper 

Original Title:  Developing a hybrid comparative optimization model for shortterm forecasting: an ‘idle time interval’ roadmap for operational units’ strategic planning 
Language:  English 
Keywords:  forecasting; optimization; efficiency; data envelopment analysis (DEA); artificial neural networks (ANNs); statistical noise 
Subjects:  C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63  Computational Techniques ; Simulation Modeling C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C45  Neural Networks and Related Topics C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61  Optimization Techniques ; Programming Models ; Dynamic Analysis D  Microeconomics > D2  Production and Organizations > D24  Production ; Cost ; Capital ; Capital, Total Factor, and Multifactor Productivity ; Capacity 
Item ID:  41573 
Depositing User:  Panagiotis Zervopoulos 
Date Deposited:  27 Sep 2012 10:25 
Last Modified:  28 Sep 2019 06:50 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/41573 