By Joe Zhu
This instruction manual represents a milestone within the development of knowledge Envelopment research (DEA). Written by way of specialists who're frequently significant individuals to DEA thought, it incorporates a choice of chapters that characterize the present cutting-edge in DEA learn. subject matters comprise distance features and their price duals, cross-efficiency measures in DEA, integer DEA, weight regulations and creation trade-offs, part research in DEA, scale elasticity, benchmarking and context-dependent DEA, fuzzy DEA, non-homogenous devices, partial input-output family, large potency, remedy of bad measures, translation invariance, stochastic nonparametric envelopment of knowledge, and international frontier index.
Focusing purely on new models/approaches of DEA, the booklet contains contributions from Juan Aparicio, Mette Asmild, Yao Chen, Wade D. prepare dinner, Juan Du, Rolf Färe, Julie Harrison, Raha Imanirad, Andrew Johnson, Chiang Kao, Abolfazl Keshvari, Timo Kuosmanen, Sungmook Lim, Wenbin Liu, Dimitri Margaritis, Reza Kazemi Matin, Ole B. Olesen, Jesus T. Pastor, Niels Chr. Petersen, Victor V. Podinovski, Paul Rouse, Antti Saastamoinen, Biresh okay. Sahoo, Kaoru Tone, and Zhongbao Zhou.
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Extra info for Data Envelopment Analysis: A Handbook of Models and Methods
13. The last column shows whether a selected project is also selected by the game cross efficiency approach of Liang et al. (2008a), which we will introduce in the next section. The difference can be due to the fact that the game cross efficiency approach is based upon the standard DEA whereas our approach is based upon the log linear frontier. 7 Game Cross Efficiency Liang et al. (2008a) develop an approach called Game Cross Efficiency, which is based upon the concept of DEA cross efficiency. As pointed out by Liang et al.
Oper Res 56:278–1288 Liang L, Wu J, Cook WD, Zhu J (2008b) Alternative secondary goals in DEA cross efficiency evaluation. Int J Prod Econ 113:1025–1030 Lim S, Zhu J (in press) DEA Cross-efficiency evaluation under variable returns to scale. J Oper Res Soc Oral M, Kettani O, Lang P (1991) A methodology for collective evaluation and selection of industrial R & D projects. Manage Sci 37(7):871–883 Sexton TR, Silkman RH, Hogan AJ (1986) Data envelopment analysis: critique and extensions. In: Silkman RH (ed) Measuring efficiency: an assessment of data envelopment analysis, vol 32.
N i=1 k = 1, . . n; i − 1, . . , m; r = 1, . . , s where “∧ denotes data in logarithmic form. 19)) “Maximum Log Cross Efficiency”. 19)) is uniquely determined; this is not the case for any of the other approaches taken up to now. Specifically, in the standard cross efficiency, the fact that alternate optimal solutions can occur, gives rise to non-unique peer ratings for a DMU, and hence the average of these (the cross efficiency score for that DMU) is not uniquely determined. 19), this fact in and of itself is immaterial.
Data Envelopment Analysis: A Handbook of Models and Methods by Joe Zhu