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.

Recently the concept of facility efficiency, which defined by DATA envelopment analysis (DEA), introduced as a location modeling objective, that provides facilities location’s effect on their performance in serving demands. By combining the DEA models with the location problem, two types of “efficiencies” are optimized: spatial efficiency which measured by finding the least cost location and allocation patterns for facilities, and the facility efficiency in serving demands which measured by DEA efficiency score. In this paper, location-allocation models with DEA in INTERVAL inputs and outputs environments are combined. A new pair of INTERVAL DEA/location models are constructed and run.

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.

In this research, INTERVAL efficiency of butchery units of Zabol city using model of INTERVAL DATA Envelopment Analysis was measured, and effective factors were investigated. DATA for this research was obtained via completing questionnaires and using randomized sampling method for the year 2010. The results showed that maximum efficiency of 50% of butcheries is less than 40% and minimum efficiency of 80% of butcheries is less than 20%. Average of minimum and maximum efficiency is 13. 5% and 47. 2%, respectively. Also interaction effects of variables were investigated on average of extensions efficiency. The results of linear regression showed that experience, study and Zabol sheep race had significant and positive correlation with efficiency. According to low efficiency of butchery units of Zabol city and consumers emphasis on seeing slaughter, livestock sale units should be established with slaughter units under the control of hygiene and health department, and meat hygienic distribution units should also be established. Since manager’ s experience has a considerable effect on efficiency of butchery units, an increase in young managers’ information via training classes focusing on marketing and meat grade is necessary.

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.

This Paper deals with calculating the minimal and maximal shares of individuals or organizations based on different criteria. Suppose that players are selfish and the score for each criterion for a player is an INTERVAL. Each player makes any possible effort to bring about his or her ideal condition. In this paper a new scheme to calculate the minimal cost - benefit ratio for INTERVAL DATA is offered. A player who has minimal cost - benefit ratio is the successful player in obtaining favorite cost and benefits. In this scheme also new models have been proposed to avoid zero weight occurrence.