In the classical DATA envelopment analysis (DEA) models, inputs and outputs are assumed as known variables, and these models cannot deal with unknown amounts of variables directly. In recent years, there are few researches on handling missing DATA. This paper suggests a new INTERVAL based approach to apply missing DATA, which is the modified version of Kousmanen (2009) approach. First, the proposed approach suggests using an acceptable range for missing inputs and outputs, which is determined by the decision maker (DM). Then, applying the least favourable bounds of missing DATA along with using the proposed range is suggested in estimating the production frontier. A DATA set is used to illustrate the approach.

DATA envelopment analysis (DEA) is a non-parametric technique for evaluation of relative efficiency of decision making units described by multiple inputs and outputs. It is based on solving linear programming problems. Since 1978 when basic DEA model was introduced many its modifications were formulated. Among them are two or multi-stage models with serial or parallel structure often called network DEA models that are widely discussed in professional community in the last years. The exact known inputs and outputs are required in these DEA models. However, in the real world, the concern is systems with INTERVAL (bounded) DATA. When we incorporate such INTERVAL DATA into multi-stage DEA models, the resulting DEA model becomes a non-linear programming problem. In this study, we suggest an approach to measure the efficiency of series and parallel systems with INTERVAL DATA that preserves the linearity of DEA model. Also, the INTERVAL DEA models are proposed to measure the lower and upper bounds of the efficiency of each DMU with INTERVAL DATA.

DATA envelopment analysis (DEA) is a method for recognizing the efficient frontier of decision making units (DMUs). This paper presents a Context-dependent DEA which uses the INTERVAL inputs and outputs. Context-dependent approach with INTERVAL inputs and outputs can consider a set of DMUs against the special context. Each context shows an efficient frontier including DMUs in particular levels. The Context-dependent DEA with INTERVAL inputs and outputs can measure (i) the attractiveness when DMUs showing weaker performance are selected as an appraisal context, and (ii) the INTERVAL progress when DMUs showing better performance are selected as the appraisal context.

In this paper, we introduce the revenue efficiency INTERVAL of a DMU. The revenue INTERVAL DEA model has been formulated to obtain an efficiency INTERVAL in which the lower bound and upper bound of this INTERVAL are respectively, the efficiency obtained from pessimistic and optimistic viewpoints.