Abstract
A production process transforming multiple inputs to different outputs is considered in conventional data envelopment analysis (DEA) models. In various settings, however, there are factors that simultaneously play the roles of both input and output called dual-role factors. In some situations, additional information is available to impose on a DEA model with dual-role factors, or the decision maker is forced to impose some restrictions regarding the importance of dual-role factors on the model. Toward this end, the current research employs two different weighting methods to introduce various weighted DEA models in the presence of dual-role factors. To strengthen the accuracy of the new models, their properties are discussed. Then, each new model is illustrated in details by a numerical example. Moreover, to show that the new models are applicable, they are applied to the Iranian banking sector. To do this, 20 bank branches which have dual-role factors are assessed. At last, to show the outcome of weight restrictions, the results obtained by each new model are compared with those from Cook and Zhu’s model [Eur. J. Oper. Res. 180 (2007) 692–699].
Highlights
Preliminary data envelopment analysis (DEA) proposed by Charnes et al [9] is a non-parametric frontier estimation methodology on the basis of linear programming problems
The results show that the second dual-role factor, deposit, is an output factor with the contributions 60%, 85%, and 50%
In the case that they should have explicit roles to improve the system performance, the results obtained show that the loan should be considered as an input factor and the deposit as an output factor
Summary
Preliminary data envelopment analysis (DEA) proposed by Charnes et al [9] is a non-parametric frontier estimation methodology on the basis of linear programming problems.
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