Cost efficiency measures the cost of resource by output production. While conventional cost efficiency MODELS set targets separately for each DMU, There are cases where the Central decision making is seeking the above targets, and at the same tries to obtain the target of Min cost efficiency for the total consumption. In this paper we consider that there is a centralized decision maker (DM). In such case, the DM has an interest in minimizing the cost efficiency of individual units at the same time it wants that total input consumption is reduced or output production is increase. We proposed new centralized resource cost efficiency model measurement on the base of Farrell cost efficiency model and Centralized resource allocation of Lozano and Villa model that will reduce resource allocation total cost efficiency, however it is possible for some inputs to increase. The preferences and advantages of this model are present some examples.

In this paper, different input-oriented ratio-based DEA (DEA-R-I) MODELS are proposed. By presenting the envelopment-additive-enhanced Russell model based on the DEA-R-I form, each decision making unit is evaluated and its efficiency score is calculated. Also, by presenting the central resource allocation model based on the DEA-R-I form, a suitable benchmark for all DMUs is proposed by solving a linear programming problem. Finally, the efficiency scores obtained for the data of 20 commercial bank branches by each of the proposed MODELS are compared.

The portfolio is a perfect combination of stock or assets, which an investor buys them. The objective of the portfolio is to divide the investment risk among several shares. Using non-parametric DEA and DEA-R methods can be of great significance in estimating portfolio. In the present paper, the efficient portfolio is estimated by using non-radial DEA and DEA-R MODELS. By proposing nonradial MODELS in DEA-R when there is ratio data the efficient portfolio is determined. At the end of the study, an applicatory example based on article [2] with non-radial DEA and DEA-R MODELS has been conducted and results are presented.

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 method to estimate the relative eciency of decision making units (DMUs) performing similar tasks in a production system that consumes multiple inputs to produce multiple outputs. So far, a number of DEA MODELS have been developed: The CCR model and the BCC model are well known as basic DEA MODELS. The generic regularity condition for the CCR model is introduced so that each weakly ecient decision making unit in the CCR model of data envelopment analysis is also CCR-ecient.In this study, we give a general regularity condition under which each weakly ecient decision making unit in the BCC model and Additive model of data envelopment analysis is also BCC-ecient and Additive-ecient, respectively.

As an effective method for evaluating efficiency, cross-efficiency evaluation has been widely used to assess the performance of Decision-Making Units (DMUs). However, non-uniqueness of the optimal weights problem reduces efficiency of this method. To address this problem, scholars have proposed using secondary goals and they have developed numerous MODELS with this consideration. In this paper, two new secondary goal MODELS are presented in order to further extend the above-mentioned existing MODELS. The proposed altruism cross-efficiency model is speci cally applicable when the evaluated DMU cooperates with other DMUs, since it maximizes the peer-efficiency scores of other DMUs. On the other hand, the exclusiveness cross-efficiency model is utilized when the evaluated DMU is competing with other DMUs. This model minimizes the peer-efficiency scores of other DMUs. Compared with the existing approaches, our approach ensures that the peer-efficiencies generated by each DMU are maximized (or minimized) under the premise that the minimum (or maximum) peer-efficiency among the DMUs is maximized (or minimized). More importantly, our proposed algorithm guarantees the uniqueness of the optimal weights and the set of cross-efficiency scores. Two numerical examples were used to verify the effectiveness of the proposed model.