تعداد نشریات | 418 |
تعداد شمارهها | 9,997 |
تعداد مقالات | 83,560 |
تعداد مشاهده مقاله | 77,801,156 |
تعداد دریافت فایل اصل مقاله | 54,843,815 |
Measurement of Inefficiency Slacks in Network Data Envelopment Analysis | ||
Theory of Approximation and Applications | ||
مقاله 4، دوره 13، شماره 1، مرداد 2019، صفحه 43-66 اصل مقاله (405.37 K) | ||
نوع مقاله: Research Articles | ||
نویسندگان | ||
Hossein Azizi* 1؛ Alireza Amirteimoori2؛ Sohrab Kordrostami3 | ||
1Department of Applied Mathematics, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran | ||
2Department of Applied Mathematics, Rasht Branch, Islamic Azad University, Rasht, Iran | ||
3Department of Applied Mathematics, Lahijan Branch, Islamic Azad University, Lahijan, Iran | ||
چکیده | ||
The two-stage data envelopment analysis models show the performance of individual processes and thus, provide more information for decision-making compared with conventional one-stage models. This article presents a set of additive models (optimistic and pessimistic) to measure inefficiency slacks in which observations are shown with crisp numbers. In the concept of pessimistic efficiency, DMU with balanced input and output data can be scored as efficient. Since pessimistic efficiency represents the minimum efficiency that is guaranteed in any unfavorable conditions, the assessment based on this efficiency is in compliance with our natural meaning, especially in risk-averse situations. Therefore, pessimistic efficiency solely can play a useful role in the DMU ranking. However, it is not a good idea to ignore optimistic efficiency. Hence, it is an inevitable necessity to integrate different performance sizes in order to achieve an overall performance assessment for each DMU. An example of resin manufacturer companies in Iran is presented to explain how to calculate the system and process inefficiency slacks. | ||
کلیدواژهها | ||
Data envelopment analysis؛ inefficiency slacks؛ series systems؛ optimistic and pessimistic viewpoints؛ overall performance | ||
مراجع | ||
[1]A. Amirteimoori, S. Kordrostami, H. Azizi, Additive models for
network data envelopment analysis in the presence of shared resources, Transportation Research Part D: Transport and Environment 48 (2016) 411-424. [2]H. Azizi, H. Ganjeh Ajirlu, Measurement of the worst practice of decisionmaking units in the presence of non-discretionary factors and imprecise data, Applied Mathematical Modelling 35 (9) (2011) 4149-4156. [3]A. Charnes, W.W. Cooper, E. Rhodes, Measuring the eciency of decision making units, European Journal of Operational Research 2 (6) (1978) 429444. [4]W.D. Cook, J. Zhu, G. Bi, F. Yang, Network DEA: Additive eciency decomposition, European Journal of Operational Research 207 (2) (2010) 1122-1129. [5]T. Entani, Y. Maeda, H. Tanaka, Dual models of interval DEA and its extension to interval data, European Journal of Operational Research 136 (1) (2002) 32-45. [6]R. Fare, S. Grosskopf, Network DEA, Socio-Economic Planning Sciences 34 (1) (2000) 35-49. [7]G.R. Jahanshahloo, M. Afzalinejad, A ranking method based on a fullinecient frontier, Applied Mathematical Modelling 30 (3) (2006) 248-260. [8]C. Kao, Eciency decomposition in network data envelopment analysis: A relational model, European Journal of Operational Research 192 (3) (2009) 949-962. [9]C. Kao, Eciency measurement for parallel production systems, European Journal of Operational Research 196 (3) (2009) 1107-1112. [10]C. Kao, S.-N. Hwang, Eciency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan, European Journal of Operational Research 185 (1) (2008) 418-429. [11]M. Khodakarami, A. Shabani, R. Farzipoor Saen, M. Azadi, Developing distinctive two- stage data envelopment analysis models: An application in evaluating the sustainability of supply chain management, Measurement 70 (2015) 62-74. 65 [12]L. Liang, W.D. Cook, J. Zhu, DEA models for two-stage processes: Game approach and eciency decomposition, Naval Research Logistics 55 (7) (2008) 643-653. [13]K. Tone, M. Tsutsui, Network DEA: A slacks-based measure approach, European Journal of Operational Research 197 (1) (2009) 243-252. [14]Y.-M. Wang, K.-S. Chin, J.-B. Yang, Measuring the performances of decision-making units using geometric average eciency, Journal of the Operational Research Society 58 (7) (2007) 929-937. | ||
آمار تعداد مشاهده مقاله: 236 تعداد دریافت فایل اصل مقاله: 167 |