Network DEA based on DEA-ratio

Abstract

Data envelopment analysis (DEA) is a technique to measure the performance of decision-making units (DMUs). Conventional DEA treats DMUs as black boxes and the internal structure of DMUs is ignored. Two-stage DEA models are special case network DEA models that explore the internal structures of DMUs. Most often, one output cannot be produced by certain input data and/or the data may be expressed as ratio output/input. In these cases, traditional two-stage DEA models can no longer be used. To deal with these situations, we applied DEA-Ratio (DEA-R) to evaluate two-stage DMUs instead of traditional DEA. To this end, we developed two novel DEA-R models, namely, range directional DEA-R (RDD-R) and (weighted) Tchebycheff norm DEA-R (TND-R). The validity and reliability of our proposed approaches are shown by some examples. The Taiwanese non-life insurance companies are revisited using these proposed approaches and the results from the proposed methods are compared with those from some other methods.

Estimating Performance Efficiency of Mining and Extracting Sectors Using DEA Models: The Case of Jordan

“…ey found that most mining companies became more efficient over time in Australia. In addition, there are many researchers have examined the efficiency performance of financial institutions based on traditional DEA [21,22]. e two-stage network was able to explain more about the variables that affect the efficiency of companies [23][24][25].…”

Construction enterprises’ green financing efficiency and its influencing factors including internal and external: Based on four-stage DEA model

“…Where i = 1,2,…, n; j = 1,2,…, m; S ij represents the jth input slack variable for the ith sample firm, f(Z i , β j ) represents the effect of external influencing factors on the input slack variable, u ij represents the management inefficiency term, v ij represents the random error term, u ij and is v ij mutually independent. Using the formula of Akbarian [ 22 ] to estimate management inefficiency and calculate the adjusted input slack variables, as shown in Eq ( 3 ): …”