Abstract

Efficiency evaluation of homogenous decision making units (DMUs), is one of the primary objectives of data envelopment analysis (DEA), in which the large number of inputs or outputs compared with the number of DMUs leads to decreased evaluation precision of DEA models. Overcoming this problem is the main issue in two-level DEA. In the present study, we use the multi objective linear programming (MOLP) method proposed by Sumpsi et al., which is based on constructing the pay-off matrix and using goal programming (GP), to investigate this problem. Furthermore, we discuss the enhanced Russell's model for evaluating and benchmarking DMUs with two-level inputs and outputs. The proposed method is applied to 24 branches of an Iranian commercial bank.

Highlights

  • Data envelopment analysis (DEA) is a scientific method for the performance analysis of different organizations in private and state-run sectors

  • The first approach is selected and one of the multi objective linear programming (MOLP) methods, proposed by Sumpsi et al ( Sumpsi et al (1997), Andre et al (2010) ) which is based on constructing the pay-off matrix and goal programming, is employed

  • Considering the type of objective function and the particular feasible region in these models, only one model is required for calculating the weights of subindices. Another issue to be addressed in such two-level input and output structures is benchmarking, which is beyond a simple comparison, since it includes knowledge of a better condition and approval of changes

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Summary

Introduction

Data envelopment analysis (DEA) is a scientific method for the performance analysis of different organizations in private and state-run sectors. Considering the type of objective function and the particular feasible region in these models, only one model is required for calculating the weights of subindices Another issue to be addressed in such two-level input and output structures is benchmarking, which is beyond a simple comparison, since it includes knowledge of a better condition and approval of changes. The ultimate goal of evaluating DMUs is to determine how changing inputs and outputs can improve performance With such a two-level structure of indices, one should select a model which can provide an appropriate interpretation for determining the changes that a subindex undergoes in a combined index. To this end, the enhanced Russell Model (Pastor et al (1999) ) is used in this paper.

Calculation of subindex weights using goal programming
Benchmarking by enhanced Russell Model after combining data
Application
Conclusion

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