* Corresponding author E-mail address: hadi_bagherzadeh@yahoo.com Int. J. Research in Industrial Engineering, pp. 19- 28 Volume 1, Number 1, 2012 International Journal of Research in Industrial Engineering journal homepage: www.nvlscience.com/index.php/ijrie Performance Evaluation Function for Benchmarking Process Using Context-Dependent Data Envelopment Analysis H. Bagherzadeh Valami 1,* , S.E. Najafi 2 , B. Farajollahzadeh 2 1 Department of mathematics, shahr-e-ray Branch, Islamic Azad University, Tehran, Iran 2 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran A B S T R A C T A R T I C L E I N F O Data envelopment analysis (DEA) is a methodology for identifying efficient frontier of decision making units (DMUs) with multiple outputs and inputs. Context-dependent DEA refers to a DEA approach where a set of DMUs are evaluated against a particular evaluation context. Each evaluation context represents an efficient frontier composed by DMUs in a specific performance level. Context-dependent DEA measures the attractiveness and the progress for each DMU. Current paper extends the context-dependent DEA by ranking all units on the basis of attractiveness and progress measures. The method is applied to measure the attractiveness and progress of 49 bank branches, and ranking them with Context-dependent DEA. Article history: Received: January 31, 2012 Revised: May 15, 2012 Accepted: June 10, 2012 Keywords: DEA, DMU, Data Envelopment Analysis 1. Introduction Data envelopment analysis (DEA), introduced by Charnes, Cooper and Rhodes (CCR) [1], is a mathematical programming method for measuring the relative efficiency of decision making units (DMUs) with multiple outputs and inputs. The most models of DEA, the best performers have efficiency score unity, and, from experience, we know that usually there are plural DMUs which have this "efficient status". Differentiating efficient DMUs is an interesting research area. The original DEA method evaluates each DMU against a set of efficient DMUs and cannot identify which efficient DMU is a better option with respect to the inefficient DMU. This is because all efficient DMUs have an efficiency score of one. Authors have proposed methods for ranking the best performers, for instance using super-efficiency DEA model. In this paper, in order to rank DMUs, we use the evaluation contexts that are obtained by partitioning the set of DMUs into several levels of efficiency, and rank all DMUs with two criteria: the attractiveness and the progress. The influence of all DMUs, both efficient and inefficient, in ranking is this method’s preference. 2. Data envelopment analysis