An extended inequality approach for evaluating decision making units with a single output
- Xiao-Li Meng^{1, 2} and
- Fu-Gui Shi^{1, 2}Email author
https://doi.org/10.1186/s13660-017-1459-z
© The Author(s) 2017
Received: 16 May 2017
Accepted: 21 July 2017
Published: 29 August 2017
Abstract
In this work, an extended evaluation approach for decision making units (DMUs) with a single output is proposed. Firstly, the input and output data for each DMU are changed in the same proportion until all the outputs are equal, and then the coordinate system is established with input i as the ith coordinate axis. Secondly, in the coordinate system, the production possibility set, which is spanned by all the DMUs without the evaluated DMU, is expressed by inequalities. Moreover, the mathematical expression of the line segment joining the origin to the evaluated DMU is given. Thirdly, the efficiency measure of the evaluated DMU is obtained from the relationship between the production possibility set and the line segment. In order to distinguish the weak efficiency and efficiency, the partially ordered set and minimal element are introduced in the paper. Finally, an example is provided to illustrate the proposed approach.
Keywords
1 Introduction
In recent years a great variety of scholarly efforts have been directed at the development of efficiency measures. These measures illustrate whether the decision making units (DMUs) are near the production frontier. Farrell [1] seemed to be the first author who devoted his work to the study of the production frontier for evaluating productivity. A few years later, Farrell’s approach was developed to two major branches, including parametric estimation method and non-parametric estimation method. Moreover, data envelopment analysis (DEA), which is used to estimate the efficiency of the evaluated DMU relative to peer DMUs, is the basic non-parametric estimation method. Since then, many improved approaches on DEA have been proposed [2–16].
What we focus on in this paper is the DEA with a single output. The initial DEA model (CCR model), as originally presented in [17], was built on the earlier work of Farrell. This model allowed every DMU to select the most favorable weight while requiring the resulted ratios of weighted outputs to weighted inputs of all the DMUs to be not greater than 1. The CCR model is a fractional programming model and solved by transforming to a linear programming model. If the constraint \(\sum^{n}_{j=1}\lambda_{j}=1\) is adjoined to the dual CCR model, the extended model is known as BCC model [18]. Soon afterwards, Charnes et al. [19] presented the C^{2}GS^{2} model to establish foundations of DEA for Pareto-Koopmans efficient empirical production functions. Färe and Grosskopf introduced a non-parametric dual method (namely, FG model) to calculate the scale efficiency [20]. In 1990, Seiford and Thrall [21], who proposed the ST model, discussed the mathematical programming approach to frontier estimation, and examined the effect of model orientation on the efficient frontier and the effect of convexity requirements on returns to scale. In addition, the transformations between models were provided.
Unlike the previous models, we develop an extended evaluation approach to estimate the efficiencies of DMUs with a single output. Different from DEA models, the proposed approach will estimate the efficiencies of DMUs only with changed input data rather than with the original input and output data. Moreover, efficiency of the evaluated DMU is estimated by considering the relationship between the defined production possibility set and the corresponding line segment of the evaluated DMU. Furthermore, the minimal element is introduced in the paper to distinguish the weak efficiency and efficiency.
The rest of the paper is unfolded as follows. The initial DEA model, partially ordered set and minimal element are reviewed in Section 2. In Section 3, the extended evaluation approach for DMUs with a single output is proposed. In Section 4, an example is given to illustrate the presented approach. In Section 5, results and discussion are given. The paper is concluded in Section 6.
2 An introduction to DEA model and the partially ordered set
Definition 1
If the optimal objective value of the evaluated DMU is equal to 1 and there is at least one optimal solution in which the optimal weight vectors of inputs and outputs are greater than 0, then the evaluated DMU is DEA efficient.
Definition 2
If the optimal objective value of the evaluated DMU is equal to 1 and there is not any optimal solution in which the optimal weight vectors of inputs and outputs are greater than 0, then the evaluated DMU is weak DEA efficient.
Definition 3
If the optimal objective value of the evaluated DMU is less than 1, then the evaluated DMU is DEA inefficient.
Subsequently, the partially ordered set and minimal element will be introduced. Let P be a nonempty set. Any subset of the cartesian product set \(P\times P=\{(x,y)|x,y\in P\}\) is called a binary relation, denoted by R. \(a,b\in P\), \(aRb\) if and only if \((a, b)\in R\) [23].
Definition 4
- (1)
reflexivity, \(x R x\),
- (2)
antisymmetry, \(x R y\) and \(y R x\) imply \(x=y\),
- (3)
transitivity, \(x R y\) and \(y R z\) imply \(x R z\).
A nonempty set P equipped with a partial order is called a partially ordered set, or poset for short. A partial order R is traditionally replaced by ‘≤’. That is, we usually replace \(x R y\) by \(x \leq y\) which is read as ‘x is less than or equal to y’.
Definition 5
Suppose that P is a partially ordered set and \(Q\subseteq P\), \(a\in Q\) is called a minimal element of Q if \(a\geq x\) and \(x\in Q\) imply \(a=x\).
For any nonempty finite subset \(S\subseteq P\), there exists at least one minimal element \(x\in S\).
3 The extended evaluation approach
In this section, a new evaluation approach for DMUs with a single output is proposed by considering the relationship between the defined production possibility set and the line segment joining the origin to the evaluated DMU.
It should be noted that all DMUs used from this section onwards are in the form of a single output. Now suppose there are n DMUs with m inputs and one output. Especially, \(X_{k}=\{x_{k1}, \ldots, x_{km}\}\) denotes the input vector of the kth DMU with k ranging from 1 to n. Without loss of generality, \(Y_{k}=\{y_{k}\}\) is the output vector with a single element for the kth DMU. Since efficiency is independent of the changes of inputs and output by the same proportion, then we change the input and output data of each DMU in the same proportion until the output data of all DMUs are equal. The input vector and output vector of the kth DMU are transformed into \(\overline{X}_{k}=\{\bar{x}_{k1}, \ldots, \bar{x}_{km}\}\) and \(\overline{Y}_{k}=\{\bar{y}_{k}\}=\overline{Y}_{l}\), \(l=1, \ldots, n\). In order to discuss the convenience of the problem, the output state will not be considered in the evaluation approach.
- (1)
\(\overline{X}_{k}\in T_{i}\), \(k\neq i\).
- (2)
For arbitrary \(\overline{X}_{k}, \overline{X}_{l}\in T_{i}\), and \(\alpha\in[0, 1]\), we have \(\alpha\overline{X}_{k}+ (1-\alpha)\overline {X}_{l}\in T_{i}\).
- (3)
If \(\overline{X}_{k}\in T_{i}\), and \(\overline{X}_{l}\geq\overline {X}_{k}\), then \(\overline{X}_{l}\in T_{i}\).
- (4)
If \(\overline{X}_{k}\in T_{i}\), and \(\alpha\geq1\), then \(\alpha \overline{X}_{k}\in T_{i}\).
- (5)
\(T_{i}\) is the least set which satisfies the conditions (1)–(4).
3.1 The relationship between the defined production possibility set and the line segment for the evaluated DMU
To better show the proposed approach, we will consider the case with two inputs and one output. Then we change the inputs and output of each DMU in the same proportion until output data of all the DMUs are equal. Next, the coordinate system is established with input 1 and input 2 as the x and y coordinate axes. For the DMU under evaluation, the closer it gets to the coordinate origin, the higher production efficiency will be.
From Figure 1(a) we can see that the evaluated DMU E is closer to the coordinate origin than the production frontier (that is to say, the line segment OE and the production possibility set \(T_{E}\) are disjoint). There exist the optimal weight vectors of inputs and output such that the production efficiency of DMU E is higher than DMUs A, B, C and D, then DMU E is efficient for DMUs A, B, C, D and E. In other words, the evaluated DMU E is efficient if there is no solution of the inequalities of the production possibility set \(T_{E}\) and the line segment OE.
In Figure 1(b), the evaluated DMU E is located on the production frontier (namely, the line segment OE meets on the production possibility set \(T_{E}\) at a point E, and there is exactly one solution of the inequalities of the production possibility set \(T_{E}\) and the line segment OE). There are two cases of interest: (1) The evaluated DMUs E on the three solid line segments AB, BC and CD are efficient. (2) The evaluated DMUs E on the two rays issuing from the points A and D are weakly efficient [25, 26]. It is important to stress here that at least one input of the weakly efficient DMU is strictly greater than that of an efficient DMU. Moreover, if the order relation ≤ for DMUs A, B, C, D and E is reflective, antisymmetric and transitive in the coordinate system, then the set \(\{ A, B, C, D, E\}\) is a partially ordered set. In the proposed approach, with the same output for all DMUs, if an evaluated DMU is located on the production frontier, its efficiency is dependent on whether the evaluated DMU is a minimal element or not. An evaluated DMU on the production frontier is weakly efficient if it is not a minimal element, otherwise the evaluated DMU is efficient.
Refer to Figures 1(c), since DMU E is farther from the coordinate origin than the production frontier, that is, the line segment OE meets on the production possibility set \(T_{E}\) at more than one point, DMU E is located in the defined production possibility set \(T_{E}\), thus DMU E is inefficient. In such a case, the solution of inequalities of the production possibility set and the line segment is not unique.
From the analysis mentioned above, the conclusion thus noted may be recorded as
Theorem 1
- (1)
If the line segment \(OE_{k}\) and the production possibility set \(T_{k}\) are disjoint, that is, there is no solution of the inequalities of the production possibility set \(T_{k}\) and the line segment \(OE_{k}\), then the kth DMU is efficient.
- (2)
The line segment \(OE_{k}\) meets on the production possibility set \(T_{k}\) at the point \(E_{k}\), which is located on the production frontier, that is, there is exactly one solution of the inequalities of the production possibility set \(T_{k}\) and the line segment \(OE_{k}\). If the evaluated DMU \(E_{k}\) is not a minimal element of \(\{E_{1}, \ldots, E_{n}\}\) equipped with an order relation ≤, then DMU \(E_{k}\) is weakly efficient, otherwise DMU \(E_{k}\) is efficient.
- (3)
If the line segment \(OE_{k}\) meets on the production possibility set \(T_{k}\), and the number of points of intersection are greater than one, that is, the number of solutions of the inequalities of the production possibility set \(T_{k}\) and the line segment \(OE_{k}\) is greater than one, then the kth DMU is inefficient, and \(E_{k}\) is located in the production possibility set.
4 Numerical experiments
DMUs with two inputs and a single output
DMU | A | B | C | D | E |
---|---|---|---|---|---|
Input 1 | 2 | 3 | 9 | 1 | 4 |
Input 2 | 6 | 3 | 3 | 6 | 4 |
Output | 2 | 0.5 | 3 | 1 | 2 |
At first, we change the inputs and output of every DMU in the same proportion until the output data are equal to 1, and establish the coordinate system with input 1 and input 2 as the x and y coordinate axes. The dots (•) denote the corresponding DMUs with the same output.
The efficiencies of DMUs
DMU | A | B | C | D | E |
---|---|---|---|---|---|
DEA efficiency of model ( 2 ) | DEA efficient | DEA inefficient | DEA efficient | Weak DEA efficient | DEA efficient |
Efficiency of the proposed approach | Efficient | Inefficient | Efficient | Weak efficient | Efficient |
5 Results and discussion
The main purpose of this paper is to develop a new evaluation approach to estimate the efficiencies of DMUs with m inputs and a single output. The efficiency measure is independent of the changes of inputs and outputs by the same proportion for the DMUs. In such a case, we change the inputs and output of each DMU in the same proportion until all the output data are equal, and then establish an m-dimensional coordinate system with input i as the ith coordinate axis. Subsequently, in the coordinate system, the production possibility set, which is spanned by all the DMUs except the DMU under evaluation, is defined by a formula that can be prompted by inequalities. In addition, the line segment joining the origin to the evaluated DMU is employed in the proposed approach, and its expression is given. Efficiency of the evaluated DMU is estimated by the relationship between the production possibility set and the line segment. Generally speaking, for the production possibility set and the line segment of each DMU, there are three intersecting results which can be obtained from the graphical method or the analytical method. In order to determine the efficiency of the evaluated DMU, the theorem is established to elucidate the relationship between the intersecting results and the efficiencies. Moreover, the partially ordered set and minimal element are used to distinguish the weak efficiency and efficiency. Finally, the use of the proposed approach is illustrated by means of an example.
6 Conclusions
The results of the proposed approach are consistent with the results of the DEA model. It is worthy of note that the inequality approach can also be applied to super-efficiency DEA. If there is no solution for the inequalities, the evaluated DMU is super-efficient. If the solution of inequalities is not unique, the evaluated DMU is inefficient. If there is exactly one solution, and the evaluated DMU is a minimal element of all the DMUs, then the evaluated DMU is efficient, otherwise the evaluated DMU is weakly efficient.
Declarations
Acknowledgements
This research was supported by the National Natural Science Foundation of China (11371002) and Specialized Research Fund for the Doctoral Program of Higher Education (20131101110048).
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
References
- Farrell, MJ: The measurement of productive efficiency. J. R. Stat. Soc. A 120, 253-281 (1957) View ArticleGoogle Scholar
- Banker, RD, Morey, RC: Efficiency analysis for exogenously fixed inputs and outputs. Oper. Res. 34, 513-520 (1986) View ArticleMATHGoogle Scholar
- Charnes, A, Cooper, WW, Wei, QL, Huang, ZM: Cone ratio data envelopment analysis and multi-objective programming. Int. J. Syst. Sci. 20, 1099-1118 (1989) MathSciNetView ArticleMATHGoogle Scholar
- Wei, QL, Yan, H: Congestion and return to scale in data envelopment analysis. Eur. J. Oper. Res. 153, 641-660 (2004) MathSciNetView ArticleMATHGoogle Scholar
- Meng, XL, Shi, FG: An extended DEA with more general fuzzy data based upon the centroid formula. J. Intell. Fuzzy Syst. 33, 457-465 (2017) View ArticleGoogle Scholar
- Andersen, P, Petersen, NC: A procedure for ranking efficient units in data envelopment analysis. Manag. Sci. 39, 1261-1264 (1993) View ArticleMATHGoogle Scholar
- Lins, MPE, Gomes, EG, Soares de Mello, JCCB, Soares de Mello, AJR: Olympic ranking based on a zero sum gains DEA model. Eur. J. Oper. Res. 148, 312-322 (2003) View ArticleMATHGoogle Scholar
- Yang, F, Wu, DD, Liang, L, Liam, O: Competition strategy and efficiency evaluation for decision making units with fixed-sum outputs. Eur. J. Oper. Res. 212, 560-569 (2011) View ArticleMATHGoogle Scholar
- Saraçli, S, Kiliç, İ, Doǧan, İ, Gazeloǧlu, C: An application of data envelopment analysis on marble factories. J. Inequal. Appl. 2013, Article ID 139 (2013) MathSciNetView ArticleMATHGoogle Scholar
- Yang, M, Li, YJ, Chen, Y, Liang, L: An equilibrium efficiency frontier data envelopment analysis approach for evaluating decision-making units with fixed-sum outputs. Eur. J. Oper. Res. 239, 479-489 (2014) MathSciNetView ArticleMATHGoogle Scholar
- Banker, RD, Chang, H, Zheng, Z: On the use of super-efficiency procedures for ranking efficient units and identifying outliers. Ann. Oper. Res. 250, 21-35 (2017) MathSciNetView ArticleMATHGoogle Scholar
- Yang, M, Li, YJ, Liang, L: A generalized equilibrium efficient frontier data envelopment analysis approach for evaluating DMUs with fixed-sum outputs. Eur. J. Oper. Res. 246, 209-217 (2015) MathSciNetView ArticleMATHGoogle Scholar
- Zanella, A, Camanho, AS, Dias, TG: Undesirable outputs and weighting schemes in composite indicators based on data envelopment analysis. Eur. J. Oper. Res. 245, 517-530 (2015) MathSciNetView ArticleMATHGoogle Scholar
- Aleskerov, F, Petrushchenko, V: DEA by sequential exclusion of alternatives in heterogeneous samples. Int. J. Inf. Technol. Decis. Mak. 15, 5-22 (2016) View ArticleGoogle Scholar
- Kao, C: Measurement and decomposition of the Malmquist productivity index for parallel production systems. Omega 67, 54-59 (2017) View ArticleGoogle Scholar
- Mardani, A, Zavadskas, EK, Streimikiene, D, Jusoh, A, Khoshnoudi, M: A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency. Renew. Sustain. Energy Rev. 70, 1298-1322 (2017) View ArticleGoogle Scholar
- Charnes, A, Cooper, WW, Rhodes, E: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429-444 (1978) MathSciNetView ArticleMATHGoogle Scholar
- Banker, RD, Charnes, A, Cooper, WW: Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag. Sci. 30, 1078-1092 (1984) View ArticleMATHGoogle Scholar
- Charnes, A, Cooper, WW, Golany, B, Seiford, L, Stutz, J: Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. J. Econom. 30, 91-107 (1985) MathSciNetView ArticleMATHGoogle Scholar
- Färe, R, Grosskopf, S: A nonparametric cost approach to scale efficiency. Scand. J. Econ. 87, 594-604 (1985) View ArticleMATHGoogle Scholar
- Seiford, LM, Thrall, RM: Recent development in DEA: the mathematical programming approach to frontier analysis. J. Econom. 46, 7-38 (1990) MathSciNetView ArticleMATHGoogle Scholar
- Charnes, A, Cooper, WW: Programming with linear fractional functionals. Nav. Res. Logist. 9, 181-186 (1962) MathSciNetView ArticleMATHGoogle Scholar
- Davey, BA, Priestley, HA: Introduction to Lattices and Order, pp. 1-18. Cambridge University Press, Cambridge (2002) View ArticleMATHGoogle Scholar
- Yu, G, Wei, QL, Brockett, P, Zhou, L: Construction of all DEA efficient surfaces of the production possibility set under the generalized data envelopment analysis model. Eur. J. Oper. Res. 95, 491-510 (1996) View ArticleMATHGoogle Scholar
- Wei, QL, Yan, H: Characteristics and structures of weak efficient surfaces of production possibility sets. J. Math. Anal. Appl. 327, 1055-1074 (2007) MathSciNetView ArticleMATHGoogle Scholar
- Charnes, A, Cooper, WW, Thrall, RM: A structure for classifying and characterizing efficiency and inefficiency in data envelopment analysis. J. Product. Anal. 2, 197-237 (1991) View ArticleGoogle Scholar