An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units
This study proposes a non-parametric efficiency frontier analysis method based on artificial neural network (ANN) and genetic algorithm clustering ensemble (GACE) for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. The propose...
| Main Authors: | , , , |
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| Format: | Journal Article |
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Chapman & Hall
2011
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| Online Access: | http://www.springerlink.com/content/y8r28q8811301250/ http://hdl.handle.net/20.500.11937/49593 |
| _version_ | 1848758273188036608 |
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| author | Azadeh, A. Saberi, Morteza Anvari, M. Mohamadi, M. |
| author_facet | Azadeh, A. Saberi, Morteza Anvari, M. Mohamadi, M. |
| author_sort | Azadeh, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This study proposes a non-parametric efficiency frontier analysis method based on artificial neural network (ANN) and genetic algorithm clustering ensemble (GACE) for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. The proposed ANN GA algorithm is able to find a stochastic frontier based on a set of input–output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. Furthermore, it uses a similar approach to econometric methods for calculating the efficiency scores. Moreover, the effect of the return to scale of decision making unit (DMU) on its efficiency is included and the unit used for the correction is selected based on its scale (under constant return to scale assumption). Also, in this algorithm, GA is used to cluster DMUs to increase DMUs’ homogeneousness. It should be noted that data envelopment analysis (DEA) is sensitive to the presence of the outliers and statistical noise. It is also not capable of performing prediction and forecasting. This is shown by two examples related to outlier situations. However, the proposed algorithm is capable of handling outliers and noise and DEA is used as a benchmark to show advantages of the proposed algorithm. Also, the proposed algorithm and conventional algorithm are compared in viewpoint of DEA through statistical t-test. The proposed approach is applied to a set of actual conventional power plants to show its applicability and superiority. |
| first_indexed | 2025-11-14T09:41:22Z |
| format | Journal Article |
| id | curtin-20.500.11937-49593 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:41:22Z |
| publishDate | 2011 |
| publisher | Chapman & Hall |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-495932017-03-15T22:55:22Z An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units Azadeh, A. Saberi, Morteza Anvari, M. Mohamadi, M. Decision making units Genetic algorithm Artificial neural network Performance assessment This study proposes a non-parametric efficiency frontier analysis method based on artificial neural network (ANN) and genetic algorithm clustering ensemble (GACE) for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. The proposed ANN GA algorithm is able to find a stochastic frontier based on a set of input–output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. Furthermore, it uses a similar approach to econometric methods for calculating the efficiency scores. Moreover, the effect of the return to scale of decision making unit (DMU) on its efficiency is included and the unit used for the correction is selected based on its scale (under constant return to scale assumption). Also, in this algorithm, GA is used to cluster DMUs to increase DMUs’ homogeneousness. It should be noted that data envelopment analysis (DEA) is sensitive to the presence of the outliers and statistical noise. It is also not capable of performing prediction and forecasting. This is shown by two examples related to outlier situations. However, the proposed algorithm is capable of handling outliers and noise and DEA is used as a benchmark to show advantages of the proposed algorithm. Also, the proposed algorithm and conventional algorithm are compared in viewpoint of DEA through statistical t-test. The proposed approach is applied to a set of actual conventional power plants to show its applicability and superiority. 2011 Journal Article http://hdl.handle.net/20.500.11937/49593 http://www.springerlink.com/content/y8r28q8811301250/ Chapman & Hall restricted |
| spellingShingle | Decision making units Genetic algorithm Artificial neural network Performance assessment Azadeh, A. Saberi, Morteza Anvari, M. Mohamadi, M. An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units |
| title | An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units |
| title_full | An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units |
| title_fullStr | An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units |
| title_full_unstemmed | An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units |
| title_short | An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units |
| title_sort | integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units |
| topic | Decision making units Genetic algorithm Artificial neural network Performance assessment |
| url | http://www.springerlink.com/content/y8r28q8811301250/ http://hdl.handle.net/20.500.11937/49593 |