An integrated approach based on DEA and AHP

Abstract

This research proposes a theoretical framework to assess the performance of Decision Making Units (DMUs) by integrating the Data Envelopment Analysis (DEA) and Analytic Hierarchy Process (AHP) methodologies. According to this, we consider two sets of weights of inputs and outputs under hierarchical structures of data. The first set of weights, represents the best attainable level of efficiency for each DMU in comparison to other DMUs. This level of efficiency can be less than or equal to that of obtaining from a traditional DEA model. The second set of weights reflects the priority weights of inputs and outputs for all DMUs, using AHP, in the DEA framework. We assess the performance of each DMU in terms of the relative closeness to the priority weights of inputs and outputs. For this purpose, we develop a parametric distance model to measure the deviations between the two sets of weights. Increasing the value of a parameter in a defined range of efficiency loss, we explore how much the deviations can be improved to achieve the desired goals of the decision maker. This may result in various ranking positions for each DMU in comparison to the other DMUs. To highlight the usefulness of the proposed approach, a case study for assessing the financial performance of eight listed companies in the steel industry of China is carried out.