Developing Common Set of Weights with Considering Nondiscretionary Inputs and Using Ideal Point Method

Abstract

Data envelopment analysis (DEA) is used to evaluate the performance of decision making units (DMUs) with multiple inputs and outputs in a homogeneous group. In this way, the acquired relative efficiency score for each decision making unit lies between zero and one where a number of them may have an equal efficiency score of one. DEA successfully divides them into two categories of efficient DMUs and inefficient DMUs. A ranking for inefficient DMUs is given but DEA does not provide further information about the efficient DMUs. One of the popular methods for evaluating and ranking DMUs is the common set of weights (CSW) method. We generate a CSW model with considering nondiscretionary inputs that are beyond the control of DMUs and using ideal point method. The main idea of this approach is to minimize the distance between the evaluated decision making unit and the ideal decision making unit (ideal point). Using an empirical example we put our proposed model to test by applying it to the data of some 20 bank branches and rank their efficient units.

1. Introduction

Data envelopment analysis (DEA) which was first proposed by Charnes et al. [1] and developed by Banker et al. [2] is a nonparametric technique for measuring the efficiency of a homogeneous group of decision making units (DMUs) on the basis of multiple inputs and outputs based on observed data [3–7]. DEA provides weights that are DMU-specific and permits individual circumstances of operation of the DMUs and for each DMU, it provides efficiency scores in the form of a ratio of a weighted sum of the outputs to a weighted sum of the inputs [8].

This method was applied to evaluate productivity and performance of airports, efficiency of air force maintenance units, hospitals, university departments, schools, industries, banks, products and services, strategic decision making, and technologies [9].

On the basis of various assumptions, a number of different models have been developed. The DEA models may be generally classified into radial and nonradial models. The radial models include the CCR and the BCC models, and the nonradial models include the additive model, the multiplication model, the range-adjusted measure (RAM), and the slack-based measure (SBM) [5, 10, 11].

Fundamental assumptions of the original DEA models [12] are that inputs and outputs are measured by exact values or are factual and definite factors [4] and assume that the assessed units (DMUs) are homogeneous. In other words, they perform the same tasks with similar objectives, consume similar inputs and similar outputs, and operate in similar operational environments and generally called discretionary factors [6]. However, in the real world situations and in many applications of the efficiency evaluation of the units, the assumption of homogeneous environments may be violated and the factors that describe the differences in the environments may need to be included in the analysis. These factors as well as other factors that are beyond the control of the DMU’s management, frequently called “exogenously fixed” or nondiscretionary, also need to be considered. Some examples of nondiscretionary factors in the DEA literature are the number of competitors in a restaurant chain and snowfall or weather in evaluating the efficiency of maintenance units and so forth [6, 9, 13].

The efficiency scores of decision making units, when DEA models are used, are between zero and one inclusively [3, 5]. DEA successfully divides them into two categories: efficient DMUs and inefficient DMUs. A ranking for inefficient DMUs is given; however, efficient DMUs cannot be ranked [7]. In order to differentiate these efficient units, a variety of methods or “ranking efficient units” in the DEA are proposed [3]. For example, Anderson and Petersen [14] and Mehrabian et al. [15] introduced two of the most popular methods, namely, AP and MAJ. Cook et al. [16] divided efficient units with equal scores on the boundary, by imposing the restrictions on the weights in a DEA analysis. Jahanshahloo et al. [17] introduced L1-norm approach that removes some deficiencies arising from AP and MAJ but fails to rank nonextreme DMUs. Liu and Hsuan Peng [18] introduced a common set of weights (CSW) to create the best efficiency score of one group composed of efficient DMUs. They then used this common set of weights to evaluate the absolute efficiency of each efficient DMU in order to rank it [5, 7].

Here in this paper, we use a method to rank the efficiency of DMUs and obtain the common set of weights model, that is, extended with nondiscretionary inputs to evaluate the absolute efficient DMUs. Using this common set of weights, the efficiency scores for DMUs are also obtained. In Section 2, we propose our model according to the CCR model which was initially proposed by Charnes, Cooper, and Rhodes in 1978, and we briefly review a general CSW method while the CSW with ideal point method is described in Section 3. Nondiscretionary version of the DEA model is extended in Section 4. Section 5 includes the extended proposed mode and in Section 6, we apply the model in empirical example. Finally Section 7 includes the paper’s conclusions and future research ideas.

2. CCR Model

Using the traditional denotations in DEA, we assume that there are a set of 𝑛 DMUs, and each DMU_𝑗(𝑗=1,…,𝑛)produces 𝑠 different outputs using 𝑚 different inputs which are denoted by 𝑥_𝑖𝑗(𝑖=1,…,𝑚) and 𝑦_𝑟𝑗(𝑟=1,…,𝑠), respectively. Here 𝑥_𝑖𝑗 and 𝑦_𝑟𝑗 are all positive [1]. For any evaluated DMU_𝑗, the efficiency score 𝐻 can be calculated by the following CCR model according to following hypotheses:𝑗is the number of decision making units (DMUs) being compared in the DEA analysis, DMU_𝑗 the 𝑗th decision making unit, 𝜃 the efficiency rating of the decision making unit being evaluated by DEA, 𝑦_𝑟𝑗 the amount of output 𝑟 used by decision making unit 𝑗, 𝑥_𝑖𝑗 the amount of input𝑖used by decision making unit 𝑗, 𝑖 the number of inputs used by the DMUs, 𝑟 the number of outputs generated by the DMUs, 𝑢_𝑟 the coefficient or weight assigned to output 𝑟 by DEA, and 𝑣_𝑖 the coefficient or weight assigned to input 𝑖 by DEA.

Also, Max𝐻=^𝑠_𝑟=1𝑢_𝑟𝑦_𝑟𝑜,subjectto^𝑠_𝑟=1𝑢_𝑟𝑦_𝑟𝑗−^𝑚_𝑖=1𝑣_𝑖𝑥_𝑖𝑗≤0,𝑗=1,2,…,𝑛,_𝑚𝑖=1𝑣_𝑖𝑥_𝑖𝑜=1,𝑢_𝑟≥0,𝑣_𝑖≥0,𝑟=1,…,𝑠,𝑖=1,…,𝑚. (1) And the dual of model (1) is Min𝐷=𝜃_𝑜subjectto^𝑛_𝑗=1𝜆_𝑗𝑥_𝑖𝑗≤𝜃_𝑜𝑥_𝑖𝑜,𝑖=1,…,𝑚._𝑛𝑗=1𝜆_𝑗𝑦_𝑟𝑗≥𝑦_𝑖𝑜𝜆,𝑟=1,…,𝑠;_𝑗≥0,𝑗=1,2,…,𝑛. (2) A characteristic of the above DEA model can be used to evaluate the relative efficiency of its favorable weights in order to calculate its maximum efficiency score for each decision making unit. We note that these efficiency scores usually lie in (0, 1]. The DEA successfully divides them into two categories: efficient DMUs and inefficient DMUs. A ranking for inefficient DMUs is given; however, DEA does not provide sufficient information about the efficient DMUs. It is noteworthy that one of the popular methods for evaluating and ranking DMUs is the common set of weights (CSW) method [7, 43].

3. Common Set of Weights

As the mathematical models in DEA are run separately for each DMU, the set of weights will be different for the various DMUs, and in some cases it is unacceptable that the same factor is accorded widely differing weights. This flexibility in selecting the weights deters the comparison among DMUs on a common base. A possible answer to this difficulty lies in the specification of a common set of weights, which was first introduced by Cook et al. [44] and Roll et al. [21] in the context of applying DEA to evaluate highway maintenance units. In other words, the major purpose for generating a common set of weights is to provide a common base for ranking the DMUs [18, 34].

It is argued by Kao and Hung [31] that using different sets of weights to classify the DMUs as efficient or inefficient is acceptable to the practitioners; however, if different sets of weights are used for ranking, most practitioners may not agree. To reduce the flexibility in selecting input and output weights, common weights have been suggested instead of variable weights for assessing the performances of DMUs. The use of common weights makes it possible to compare and rank the performances of the DMUs on the same basis [39].

Table 1 gives a brief summary of some relevant research on DEA to find CSW.

Table 1 

3.1. Common Set of Weights by Comparing with Ideal DMU (Ideal Point)

DEA was initially developed as a methodology for assessing the comparative efficiencies of organized units. In conventional DEA models each DMU in turn maximizes the efficiency score, under the constraint that none of the DMUs efficiency scores is allowed to exceed 1.0. Decision maker always intuitively takes the maximal efficiency score 1.0 as the common benchmark level for DMUs. Liu and Hsuan Peng [18] have taken advantage of this benchmark level to concretely describe the concept of the generation of common weights. By the definition of the efficiency score, the common benchmark level is one straight line with slope 1.0 that passes through the origin. Some might argue that it cares too much about distance of each DMU’s input and output itself and wonder why not consider the distance between the evaluated decision making unit and the ideal decision making unit. Here we attempt to rank efficiency of DMUs with common weights by comparison of ideal point (ideal DMU: DMU).

Definition 1. Assume that there are a set of 𝑛 DMUs. Each DMU_𝑗(𝑗=1,…,𝑛)has𝑚different inputs and 𝑠 different outputs, which are denoted by 𝑥_𝑖𝑗(𝑖=1,2,…,𝑚) and𝑦_𝑟𝑗(𝑟=1,2,…,𝑠), respectively. All the data are required to be positive just like the traditional DEA model [1]. The input data of all DMUs form an 𝑚 by 𝑛 matrix and output data form an 𝑠 by 𝑛 matrix. The smallest data of each row of input matrix is selected to be the input of the virtual ideal DMU, and the biggest data of each row of output matrix is selected to be the output of the virtual ideal DMU.
The virtual ideal DMU is a DMU with minimized inputs of all of DMUs as its input and maximized outputs of all of DMUs as its output. Generally, if we show ideal DMU with DMU=(𝑋,𝑌), then we have 𝑥_𝑖=min{𝑥_𝑖𝑗∣𝑗=1,…,𝑛}(𝑖=1,…,𝑚) and 𝑦_𝑟=max{𝑦_𝑟𝑗∣𝑗=1,…,𝑛}(𝑟=1,…,𝑠).

Definition 2. An ideal level is one straight line that passes through the origin and ideal DMU with slope 1.0.

In Figure 1, the vertical and horizontal axes are set to be the weighted sum of 𝑠 outputs and the weighted sum of 𝑚 inputs, respectively. Line ‘‘ox’’ is an ideal line representing that all the points on the line must satisfy the constraint that the weighted sum of 𝑠 outputs equals the weighted sum of 𝑚 inputs and so DMU=(∑^𝑚_𝑖=1𝑥_𝑖𝑣_𝑖́,∑^𝑠_𝑟=1𝑦_𝑟𝑢́_𝑟)is an ideal DMU. Given one set of weights 𝑢́_𝑟(𝑟=1,…,𝑠)and 𝑣_𝑖́(𝑖=1,…,𝑚), the virtual gaps, between points 𝑀 and DMU on the horizontal axis and vertical axis, are denoted by Δ^𝐼_𝑀=∑^𝑛_𝑗=1𝑣_𝑀𝑥_𝑀𝑗−∑^𝑛_𝑗=1𝑣_𝑀𝑥_min and Δ^𝑅_𝑀=∑^𝑛_𝑗=1𝑢_𝑀𝑦_max−∑^𝑠_𝑟=1𝑢_𝑀𝑦_𝑀𝑗, respectively. Similarly, for points 𝑁 and 𝐿, the gaps will be calculated (Δ^𝐼_𝑁,Δ^𝑅_𝑁andΔ^𝐼_𝐿,Δ^𝑅_𝐿). Observing that there exists a total virtual gap to the ideal point, we aim to determine an optimal set of weights 𝑢^∗_𝑟(𝑟=1,…,𝑠) and 𝑣^∗_𝑖(𝑖=1,…,𝑚) such that both points 𝑀^∗ and 𝑁^∗ below the ideal line could be as possibly close to their ideal point (DMU) on the ideal line. In other words, by adopting the optimal weights, the total virtual gapsΔ^𝐼_𝑀+Δ^𝑅_𝑀+Δ^𝐼_𝑁+Δ^𝑅_𝑁+Δ^𝐼_𝐿+Δ^𝑅_𝐿to the ideal point are the shortest to DMU.

Figure 1 

An illustration of the model for gap analysis, showing DMUs below the virtual ideal DMU.

As for the constraint, the numerator is the weighted sum of outputs plus the vertical gap ∑^𝑛_𝑗=1Δ^𝐼_𝑗 and the denominator is the weighted sum of inputs minus the horizontal virtual gap ∑^𝑛_𝑗=1Δ^𝑅_𝑗. The equations in constraints are equal to 1.0, meaning that the projection point (ideal DMU) is reached. Therefore we have the following model: MinΔ^∗=^𝑛_𝑗=1Δ^𝐼_𝑗+^𝑚_𝑗=1Δ^𝑅_𝑗subjectto^𝑚_𝑖=1𝑣_𝑖𝑥_𝑖𝑗−^𝑠_𝑟=1𝑢_𝑟𝑦_𝑟𝑗≥0,𝑗=1,2,…,𝑛_𝑚𝑖=1𝑣_𝑖𝑥_min=1;^𝑠_𝑟=1𝑢_𝑟𝑦_max𝑣=1,_𝑖,𝑢_𝑟≥𝜀≥0,𝑖=1,….,𝑚,𝑟=1,….,𝑠. (3) Then, if we let Δ^𝐼_𝑗 be ∑^𝑚_𝑖=1𝑣_𝑖𝑥_𝑖𝑗−∑^𝑚_𝑖=1𝑣_𝑖𝑥_min and let Δ^𝑅_𝑗 be ∑^𝑠_𝑟=1𝑢_𝑟𝑦_max−∑^𝑠_𝑟=1𝑢_𝑟𝑦_𝑟𝑗, model (3) is then simplified to the following linear programming (4): MinΔ^∗=^𝑛_𝑗=1^𝑚_𝑖=1𝑣_𝑖𝑥_𝑖𝑗−^𝑚_𝑖=1𝑣_𝑖𝑥_min+^𝑛_𝑗=1^𝑠_𝑟=1𝑢_𝑟𝑦_max−^𝑠_𝑟=1𝑢_𝑟𝑦_𝑟𝑗,subjectto^𝑚_𝑖=1𝑣_𝑖𝑥_𝑖𝑗−^𝑠_𝑟=1𝑢_𝑟𝑦_𝑟𝑗≥0,𝑗=1,2,…,𝑛,_𝑚𝑖=1𝑣_𝑖𝑥_min=1;^𝑠_𝑟=1𝑢_𝑟𝑦_max𝑣=1,_𝑖,𝑢_𝑟≥𝜀≥0,𝑖=1,…,𝑚,𝑟=1,…,𝑠. (4)

Definition 3. The performance of DMU_𝑗 is better than that of DMU_𝑗 ifΔ_𝑗<Δ_𝑖.

From the model (4), it is found that the distance between DMU and DMU_𝑗 is defined as Δ^𝐼DMU_𝑗=(∑^𝑚_𝑖=1𝑣_𝑖𝑥_𝑖𝑗−∑^𝑚_𝑖=1𝑣_𝑖𝑥_min)+(∑^𝑠_𝑟=1𝑢_𝑟𝑦_max−∑^𝑠_𝑟=1𝑢_𝑟𝑦_𝑟𝑗). Note that the purpose of the model (4) is to obtain an optimal solution(𝑣^∗_𝑖,𝑢^∗_𝑟)to make the total distances between all DMUs and DMU as short as possible [6, 42].

Next we find efficiency of each DMU with optimal weights. If a DMU_𝑗 is on ideal point then we use definition of the CSW efficiency score of DMU_𝑗 that was defined by the following equation (e.g., see [18, 45]): 𝜇^∗_𝑗=∑^𝑠_𝑟=1𝑦_𝑟𝑗𝑢^∗_𝑟−∑^𝑛_𝑖=1𝑧_𝑖𝑗𝑣^∗_𝑖́∑^𝑛_𝑖=1𝑥_𝑖𝑗𝑣^∗_𝑖. (5)

4. Nondiscretionary Model

Assume that there are 𝑛 DMUs, where each DMU_𝑗(𝑗=1,2,…,𝑛), uses 𝑡 different discretionary inputs, 𝑥_𝑖𝑗(𝑖=1,…,𝑡),  and 𝑘 different nondiscretionary inputs, 𝑧_𝑖𝑗(𝑖=1,…,𝑘) for 𝑡+𝑘=𝑚, to produce 𝑠 different outputs 𝑦_𝑟𝑗(𝑟=1,…,𝑠).

There are some models that incorporate nondiscretionary inputs into DEA models. Banker and Morey [45] provided the first model by modifying the constraints on the fixed factors within the DEA model. This model differs from the original CCR DEA model by breaking the link between nondiscretionary inputs and efficiency: Max𝜔=^𝑠_𝑟=1𝑢_𝑟𝑦_𝑟𝑜−^𝑘_𝑖=1𝑣_𝑖́𝑧_𝑖𝑜subjectto^𝑠_𝑟=1𝑢_𝑟𝑦_𝑟𝑗−^𝑘_𝑖=1𝑣_𝑖́𝑧_𝑖𝑗−^𝑡_𝑖=1𝑣_𝑖𝑥_𝑖𝑗≤0,𝑗=1,2,…,𝑛,_𝑡𝑖=1𝑣_𝑖𝑥_𝑖𝑜=1,𝑣_𝑖𝑢≥𝜀,𝑖=1,2,…,𝑡,_𝑟𝑣́≥𝜀,𝑟=1,2,…,𝑠,_𝑖≥0,𝑖=1,2,…,𝑘. (6) We note that there is a great similarity between fixed factor constraints and constraints on the nondiscretionary inputs while both constraints are modified. This modification is used to take the fixed factors of production under control in order to break the link between the efficiency and the fixed factors [7, 46].

Although this model allows each DMU to measure the efficiency with its favorable weights, to calculate its efficiency it may not be compared and ranked on the same basis. Moreover, some of the efficient DMUs may have their efficiency scores equal to one because of the flexibility in the selection of weights.

5. Proposed Model

To address the problems mentioned above, using ideal point (ideal DMU) method, we propose a new model to extend the existing nondiscretionary DEA model for generating common weights. This model allows us to obtain and compare the efficiency scores from multiple different angles.

Assume that there are 𝑛 DMUs, where each DMU_𝑗(𝑗=1,2,…,𝑛) uses𝑡different discretionary inputs, 𝑥_𝑖𝑗(𝑖=1,…,𝑡) and 𝑘 different nondiscretionary inputs, 𝑧_𝑖𝑗(𝑖=1,…,𝑘) for 𝑡+𝑘=𝑚, to produce 𝑠 different outputs 𝑦_𝑟𝑗(𝑟=1,…,𝑠).

Definition 1. The virtual ideal DMU is a DMU with minimized inputs of all of the DMUs as its input and maximized outputs of all of the DMUs as its output. Generally, if we show ideal DMU with DMU=(𝑋,𝑌) then we have 𝑥_𝑖=min{𝑥_𝑖𝑗∣𝑗=1,…,𝑛}(𝑖=1,…,𝑚) and 𝑦_𝑟=max{𝑦_𝑟𝑗∣𝑗=1,…,𝑛}(𝑟=1,…,𝑠). Here𝑋consists of two parts, comprehensive minimum discretionary and nondiscretionary inputs and one maximum output for ideal DMU.

For this reason, we haveDMU=(𝑋,𝑌)that 𝑥_𝑖1=min{𝑥_𝑖𝑗,∣𝑗=1,…,𝑛}(𝑖=1,…,𝑚), 𝑥_𝑖2=min{𝑧_𝑖𝑗,∣𝑗=1,…,𝑛}(𝑖=1,…,𝑚), and 𝑦_𝑟=max{𝑦_𝑟𝑗∣𝑗=1,…,𝑛}(𝑟=1,…,𝑠).

Then, according to models (4) and (6) we can construct the following model Min𝐸=^𝑛_𝑗=1^𝑚_𝑖=1𝑣_𝑖𝑥_𝑖𝑗−^𝑚_𝑖=1𝑣_𝑖𝑥_1min+^𝑛_𝑗=1^𝑚_𝑖=1𝑣_𝑖́𝑧_𝑖𝑗−^𝑚_𝑖=1𝑣_𝑖́𝑥_2min+^𝑛_𝑗=1^𝑠_𝑟=1𝑢_𝑟𝑦_max−^𝑠_𝑟=1𝑢_𝑟𝑦_𝑟𝑗subjectto^𝑚_𝑖=1𝑣_𝑖𝑥_𝑖𝑗+^𝑚_𝑖=1𝑣_𝑖́𝑧_𝑖𝑗−^𝑠_𝑟=1𝑢_𝑟𝑦_𝑟𝑗≥0,𝑗=1,2,…,𝑛,_𝑚𝑖=1𝑣_𝑖𝑥_1min=1,^𝑠_𝑟=1𝑢_𝑟𝑦_max−^𝑚_𝑖=1𝑣_𝑖́𝑥_2min𝑣=1,_𝑖,𝑢_𝑟,𝑣_𝑖́≥𝜀>0,𝑖=1,…,𝑚,𝑟=1,…,𝑠. (7)

The dual of model (7) is Max𝑇=𝛼+𝛽+𝜀^𝑚_𝑖=1𝛾_𝑖+𝜀^𝑚_𝑖=1𝛾_𝑖+𝜀^𝑠_𝑟=1𝛿_𝑟subjectto^𝑛_𝑗=1𝜆_𝑗𝑥_𝑖𝑗+𝛼𝑥_1min+𝛾_𝑖≤𝑛^𝑚_𝑖=1𝑥_𝑖𝑗−𝑥_1min,𝑖=1,2,…,𝑡._𝑛𝑗=1𝜆_𝑗𝑧_𝑖𝑗−𝛽𝑥_2min+𝛾_𝑖≤𝑛^𝑚_𝑖=1𝑧_𝑖𝑗−𝑥_2min,𝛽𝑖=1,2,…,𝑡.𝑦_max−^𝑛_𝑗=1𝜆_𝑗𝑦_𝑟𝑗+𝛿_𝑟≤𝑛𝑦_max−^𝑠_𝑟=1𝑦_𝑟𝑗,𝑟=1,2,…,𝑠,𝜆_𝑗,𝛾_𝑖,𝛾_𝑖,𝛿_𝑟≥0,𝑗=1,2,…,𝑛,𝑖=1,2,…,𝑚,𝑟=1,2,…,𝑠,𝛼,𝛽→free. (8)

6. Numerical Examples

In this section we provide an empirical study of bank performance evaluation in order to demonstrate the robustness of our method as well as having a better understanding of the performance of our proposed model.

6.1. Empirical Example

To illustrate the proposed model consider 20 bank branches in Iran with 2 discretionary inputs and 1 nondiscretionary input and 2 outputs. In Tables 2, 3, 4, and 5, we apply models (6) and (7) to evaluate efficient DMUs. The second, third, seventh, and eighth columns of Table 4 report the model (6) efficiency scores with nondiscretionary inputs and its rankings, respectively. This model allows DMUs to measure their efficiencies with various weights. Thus, the efficiencies of 20 DMUs obtained by 20 sets of weights may not be possible to be compared and ranked on the same basis, and so a common set of weights method in model (7) is utilized. The efficiencies of the 20 bank branches of model (7) with optimal weights and using (5) are shown in the fourth, fifth, ninth, and tenth columns of Table 4, respectively. Table 5 shows the weight results of the proposed model. It is evident that the new models can all be used for generating common weights. We emphasize that they all offer more reasonable results than the conventional DEA models.

Table 2 

Labels of inputs and outputs.

Table 3 

The data of 20 bank branches.

Table 4 

The results of 20 bank branches from models (5)–(7).

Table 5 

The weights of model (6).

Table 4 shows the efficiencies of all bank branches from two models. The column of model (6) gives the CCR efficiency scores with nondiscretionary inputs. Observe that there are 5 efficient DMUs with different selection weights. It is not possible to give them a full ranking.

In order to solve this problem, we propose a common set of weights model considering nondiscretionary inputs to calculate a set of optimal weights (see Table 5) for all DMUs. Using these and (5), all efficiency scores for all DMUs are calculated and ranked. The results are shown in the fourth, fifth, ninth, and tenth columns of Table 4.

Using models (7) and (5), clearly we can find an optimal set of weights for evaluating each DMU and calculate efficiency scores to rank all the bank branches completely which is preferable to that of using model (6).

The above empirical example shows that the new proposed DEA model can successfully acquire a full ranking for the DMUs. This method may be a good way for full ranking DMUs with various data since they are accustomed to a good unit of comparison.

7. Conclusions and Future Research

General DEA models are used to evaluate the relative efficiency with its favorable weights in order to calculate the efficiency score of each decision making unit. These obtained scores are between zero and one, with a possibility of some having an equal efficiency score of one (efficient DMUs) which is due to the flexibility in the selection of weights. DEA successfully divides DMUs into two categories: efficient DMUs and inefficient DMUs. Ranking of DMUs in DEA is an important phase for efficiency evaluation of DMUs. In DEA techniques, a ranking for inefficient DMUs is given. However, generally, DEA does not provide adequate information about the efficient DMUs and does not rank them.

One of the popular methods for evaluating and ranking efficiency and inefficiency DMUs is common set of weights (CSW) method, that is, the most favorable in determining the absolute efficiency for all of DMUs.

The conventional DEA methodology requires the inputs and the outputs of the DMUs to be discretionary. Nevertheless, in reality, many observations are nondiscretionary in nature. We generated a nondiscretionary version of a CSW model, that is, beyond the control of DMUs, and for this purpose we used ideal point method. The idea of this approach is to minimize the distance between the evaluated decision making unit and the ideal decision making unit (ideal point). Ranking DMUs determines the input and output weights by minimizing the distance of all DMUs and the point (ideal DMU) to get the best efficiency score. The optimal solution of this model was considered as a set of weights for all DMUs. Then DMUs were ranked according to (5). To validate our model, we used an empirical example in ranking DMUs using our proposed model.

We hope that this paper will inspire future researchers to explore relevant ideas in developing CSW method to consider various data such as nondiscretionary inputs and stochastic data.