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.

PDF
MPRA_paper_41573.pdf Download (487kB)  Preview 
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:  22. Aug 2015 07:16 
References:  Athanassopoulos, A., Curram, S., 1996. A comparison of Data Envelopment Analysis and artificial neural networks as tools for assessing the efficiency of decision making units. Journal of the Operational Research Society 47, 10001016. Banker, R.D., 1993. Maximum likelihood, consistency and Data Envelopment Analysis: A statistical foundation. Management Science 39, 12651273. Banker, R.D., Charnes, A., Cooper, W.W., 1984. Some models for estimating technical and scale inefficiencies in Data Envelopment Analysis. Management Science 30, 10781092. Banker, R.D., Gadh, V.M., Gorr, W.L., 1993. A Monte Carlo comparison of two production frontier estimation methods: Corrected ordinary least squares and Data Envelopment Analysis. European Journal of Operational Research 67, 332343. Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2, 429444. Coelli, T.J., Rao, P., O’Donnell, C.J., Battese, G., 2005. An Introduction to Efficiency and Productivity Analysis (2nd ed.). Springer, New York. Cooper, W.W., Seiford, L.M., Tone, K., 2007. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEAsolver Software (2nd ed.). Springer, New York. Costa, A., Markellos, R.N., 1997. Evaluating public transport efficiency with neural network models. Transportation Research Part C: Emerging Technologies 5, 301312. Emrouznejad, A., Parker, B., Tavares, G., 2008. Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Journal of SocioEconomics Planning Science 42(3), 151157. Ferlie, E., Lynn, L., Pollitt C., 2007. The Oxford Handbook of Public Management. Oxford University Press, New York. Hornik, K., Stinchcombe, M., White, H., 1990. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Networks 3, 551560. Hu, S.C., Chung, Y.K., Chen, Y.S., 2008. Using Hopfield neural networks to solve DEA problems. IEEE International Conference on Cybernetics and Intelligence Systems, 606611. Liang, L., Wu, D., 2005. An application of pattern recognition on scoring Chinese corporations financial conditions based on backpropagation neural network. Computers and Operations Research 32, 11151129. Maqsood, I., Abraham, A., 2007. Weather analysis using ensemble of connectionist learning paradigms. Applied Soft Computing 7, 9951004. Mostafa, M.M., 2009. Modeling the efficiency of top Arab banks: A DEAneural network approach. Expert Systems with Applications 36, 309320. Pendhakar, P., Rodger, J., 2003. Technical efficiencybased selection of learning cases to improve forecasting accuracy of neural networks under monotonicity assumption. Decision Support Systems 36, 117136. Pollitt, C., Bouckaert, G., 2004. Public Management Reform: A Comparative Analysis (2nd ed.). Oxford University Press, New York. Şeker, S., Ayaz, E., Türkcan, E., 2003. Elman’s recurrent neural network applications to condition monitoring in nuclear power plant and rotating machinery. Engineering Application of Artificial Intelligence 16, 647656. Siegelmann, H.T., 1999. Neural Networks and Analog Computation: Beyond the Turing Limit. Birkhäuser, Boston. Simar, L., 2007. How to improve the performances of DEA/FDH estimators in the presence of noise?. Journal of Productivity Analysis 28, 183201. Troutt, M.D., Rai, A., Zhang, A., 1996. The potential use of DEA for credit applicant acceptance systems. Computers & Operations Research 4, 405–408. Wang, S., 2003. Adaptive nonparametric efficiency frontier analysis: A neuralnetworkbased model. Computers & Operations Research 30, 279295. Worthington, A., Dollery, E., 2000. Measuring efficiency in local governments planning and regulatory function. Public Productivity & Management Review 23, 469485. Wu, C., Chen, X., Yang, Y., 2004. Decisionmaking modeling method based on artificial neural network and data envelopment analysis. IEEE International Conference on Geoscience and Remote Sensing Symposium. Wu, D., Yang, Z., Liang, L., 2006. Using DEAneural network approach to evaluate branch efficiency of a large Canadian bank. Expert Systems with Applications 31, 108115. Yaghoobi, R., Aryanezhad, M.B., Hosseinzadeh Lotfi, F., 2010. Application of multilayer recurrent neural network and fuzzy time series in input/output prediction of DEA models: Real Case Study of a Commercial Bank. IEEE International Conference on Computers and Industrial Engineering, 16. Yan, H., Jiang, Y., Zheng, J., Peng, C., Li, Q., 2006. A multilayer perceptronbased medical decision support system for heart disease diagnosis. Expert Systems with Applications 30, 272281. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/41573 