Notes on Sensitivity and Stability of the Classifications of Returns to Scale in Data Envelopment Analysis: A Comment

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47549/1/11123_2005_Article_2212.pd

Notes on Sensitivity and Stability of the Classifications of Returns to Scale in Data Envelopment Analysis: A Comment

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47549/1/11123_2005_Article_2212.pd

Continuous Optimization Modeling undesirable factors in efficiency evaluation

Abstract Data envelopment analysis (DEA) measures the relative efficiency of decision making units (DMUs) with multiple performance factors which are grouped into outputs and inputs. Once the efficient frontier is determined, inefficient DMUs can improve their performance to reach the efficient frontier by either increasing their current output levels or decreasing their current input levels. However, both desirable (good) and undesirable (bad) factors may be present. For example, if inefficiency exists in production processes where final products are manufactured with a production of wastes and pollutants, the outputs of wastes and pollutants are undesirable and should be reduced to improve the performance. Using the classification invariance property, we show that the standard DEA model can be used to improve the performance via increasing the desirable outputs and decreasing the undesirable outputs. The method can also be applied to situations when some inputs need to be increased to improve the performance. The linearity and convexity of DEA are preserved through our proposal

An investigation of returns to scale in data envelopment analysis

This paper discusses the determination of returns to scale (RTS) in data envelopment analysis (DEA). Three basic RTS methods and their modifications are reviewed and the equivalence between these different RTS methods is presented. The effect of multiple optimal DEA solutions on the RTS estimation is studied. It is shown that possible alternate optimal solutions only affect the estimation of RTS on DMUs which should be classified as constant returns to scale (CRS). Modifications to the original RTS methods are developed to avoid the effects of multiple optimal DEA solutions on the RTS estimation. The advantages and disadvantages of these alternative RTS methods are presented so that a proper RTS method can be selected within the context of different applications

An investigation of returns to scale in data envelopment analysis

This paper discusses the determination of returns to scale (RTS) in data envelopment analysis (DEA). Three basic RTS methods and their modifications are reviewed and the equivalence between these different RTS methods is presented. The effect of multiple optimal DEA solutions on the RTS estimation is studied. It is shown that possible alternate optimal solutions only affect the estimation of RTS on DMUs which should be classified as constant returns to scale (CRS). Modifications to the original RTS methods are developed to avoid the effects of multiple optimal DEA solutions on the RTS estimation. The advantages and disadvantages of these alternative RTS methods are presented so that a proper RTS method can be selected within the context of different applications.data envelopment analysis (DEA) returns to scale (RTS) scale efficiency