One way to increase the discrimination ability in data envelopment analysis (DEA) is to use the pessimistic view in the performance evaluation. A traditional and usual approach to move from the optimistic to the pessimistic view is to expand the production possibility set. By expanding the production possibility set, the distance between each unit can be increased from the efficiency frontier, and then a smaller number of units are located on the boundary. On the other hand, in practical applications, we are confronted with imprecise inputs and outputs. Expressions of inputs and outputs as imprecise data can give us an opportunity to use it in order to increase the efficiency discrimination. Our view of the ambiguity in the data focus on fuzzy relation. We introduce a fuzzy monotonicity assumption and construct a fuzzy production possibility set (FPPS) with varying degrees of feasibility. Using the tolerance approach a nonsymmetric fuzzy linear programming model and subsequently, a parametric DEA model is constructed. By applying this model, it will be seen that, for a specific and small tolerance of constraints, The discrimination efficiency of the units increases. Finally, we propose a procedure for ranking of DMUs and employ it to rank Iranian national universities. Manuscript profile

Finding the Most Efficient Decision-Making Unit (DMU) provides more information about efficient DMUs in data envelopment analysis (DEA). Hence, in recent years, many mixed-integer linear programming (MILP) models based on a common set of weights have been proposed to determine the most efficient DMU. This paper introduces another MILP model to find the most efficient DMU. In this model, we use a numerical parameter to increase the discrimination power of the proposed model. To illustrate the various potential applications of the proposed model, we compare the performance of our model with the other three models using two real numerical examples. Manuscript profile

The identification of undesirable congestion is important to avoid a cost increase and a shortage of generation. However, the identification of desirable congestion or eco-technology innovation, in systems that produce undesirable outputs is much more important than that of undesirable congestion from the perspective of environmental assessment. In this paper, we propose an approach to identify desirable congestion that can be effectively used to reduce the amount of undesirable outputs so that systems such as electric power companies satisfy a governmental standard on environmental protection. Thus, the identification of desirable congestion assists us in determining which technology should be invested to facilitate eco-technology innovation and its related engineering management for a future sustainable economic growth. We use the proposed approach to study the pollutants of two empirical data in China and USA. Manuscript profile

The weights generated by the common weights approach provide a common criterion for ranking the decision-making units (DMUs) in data envelopment analysis (DEA). Existing common weights models in DEA are either very complicated or unable to produce a full ranking for DMUs. This paper proposes a new compromise solution model to seek a common set of weights for full ranking for DMUs. The maximum inefficiency scores calculated from the standard DEA model are regarded as the anti-ideal solution for the DMUs to avoid. A common set of weights that produces the vector of inefficiency scores for the DMUs furthest to the anti-ideal solution is sought. The discrimination power of the new model is tested using two numerical examples and its potential application for fully ranking DMUs is illustrated. Manuscript profile

The cross-efficiency method in data envelopment analysis (DEA) has widely been used as a suitable utility for ranking decision-making units (DMUs). In cross-efficiency, the average of n efficiency values for each DMU is considered as the overall efficiency score. Another method based on the concept of cross-efficiency for calculating the efficiency score for each DMU is to use the average comparable weights to calculate the efficiency, which is called the cross-weight evaluation. In the cross-weight method, as in the cross-efficiency method, there is the issue of multiple optimal solutions. In this paper, for overcoming this issue, we use the neutral strategy for cross-weight evaluation. Unlike the aggressive and benevolent formulations, the neutral way is only concerned with its own interests and is indifferent to other DMUs. This study proposes a new cross-weight evaluation by maximizing the minimum of the output weights while keeping the efficiency of the unit under evaluation unchanged. Numerical examples are provided to illustrate the potential application of this new model and its effectiveness in ranking DMUs. Manuscript profile

In this paper, we present a recurrent neural network model for solving CCR Model in Data Envelopment Analysis (DEA). The proposed neural network model is derived from an unconstrained minimization problem. In the theoretical aspect, it is shown that the proposed neural network is stable in the sense of Lyapunov and globally convergent to the optimal solution of CCR model. The proposed model has a single-layer structure. A numerical example shows that the proposed model is effective to solve CCR model in DEA. Manuscript profile

Data envelopment analysis (DEA) is a non-parametric programming method for evaluating the relative efficiency of a set of peer decision-making units (DMUs) with multiple inputs and multiple outputs. The DEA cross-efficiency method is a well-known method that use to evaluate and ranking a set of peer decision-making units. Whenever a DMU intends to evaluate other DMUs, it faces the problem of non-uniqueness optimal weights of DEA models. Because different weights give us different cross-scores and subsequently different cross-efficiencies scores and this will confuse the decision-maker to make an ultimate decision. The main drawback of this method is the alternate optimal solution set of the DEA model. The main purpose of this study is to propose an approach to this problem to generate non-dominated DEA cross-efficiency scores. We propose a linear programming secondary goal model to select a set of optimal weights for each DMU. Our proposed method is not only simpler than other methods presented with the same purpose, but also does not go beyond the main method. Manuscript profile