The role of hyperplanes for characterizing suspicious units in DEA

In this paper, the suspicious units including anchor, terminal, and exterior units are investigated as important subsets of vertex units. Based on the concept of separating hyperplanes, an alternative definition of vertex units in data envelopment analysis is presented. Moreover, an advanced mathematical model for obtaining the separating hyperplane that splits up a vertex unit from the other units is proposed. Utilizing the core concept of separating hyperplanes, the special geometry of terminal units enables us to introduce a new definition for terminal units. Thus, some theorems have been proved which provide necessary and sufficient conditions for obtaining terminal units. We made use of the concept of supporting hyperplanes to provide a basic definition for exterior units and present a careful model for discovering exterior units. Also, based on the concept of supporting hyperplanes, different definitions of anchor units have been represented. Finally, the relationship between the sets of exterior, terminal and anchor units have been demonstrated in a theorem.

     

Cost efficiency measures in data envelopment analysis with data uncertainty

This paper extends the classical cost efficiency (CE) models to include data uncertainty. We believe that many research situations are best described by the intermediate case, where some uncertain input and output data are available. In such cases, the classical cost efficiency models cannot be used, because input and output data appear in the form of ranges. When the data are imprecise in the form of ranges, the cost efficiency measure calculated from the data should be uncertain as well. So, in the current paper, we develop a method for the estimation of upper and lower bounds for the cost efficiency measure in situations of uncertain input and output data. Also, we develop the theory of efficiency measurement so as to accommodate incomplete price information by deriving upper and lower bounds for the cost efficiency measure. The practical application of these bounds is illustrated by a numerical example.     

A comment on “Returns to scale and scale elasticity in data envelopment analysis”

Fukuyama [Fukuyama, H., 2000. Returns to scale and scale elasticity in data envelopment analysis. European Journal of Operational Research 125, 93–112] provided some characterizations of scale elasticity and returns to scale in DEA. In this paper, we present some counterexamples and comments to the main lemma of the afore-mentioned paper, based on which Fukuyama’s idea has been built.     

A simplified version of the DEA cost efficiency model

This short paper deals with a current cost efficiency model and decreases the number of its constraints as well as its variables, which leads to a strong reduction in the computational requirements.     

Preference score of units in the presence of ordinal data

This study deals with the ordinal data in the performance analysis framework and provides a weight-restricted DEA model to obtain the preference score of each unit under assessment. The obtained scores are used to rank DMUs. Furthermore, to decrease the complexity of the provided model, the number of the constraints is decreased by some linear transformations.     

Stability of the classification of returns to scale in FDH models

This paper deals with the estimation of returns to scale (RTS) in free disposal hull (FDH) models and provides some stability intervals for preserving the RTS classification. It has been shown that the proposed stability intervals can be obtained via a polynomial-time algorithm based on the calculation of certain ratios of inputs and outputs, without solving any mathematical programming problem. The results of the study have been proved via some lemmas and theorems and have been illustrated by numerical examples and a real application.     

An equitable method for allocating fixed costs by using data envelopment analysis

This paper concerns the shared cost allocation problem by using Data Envelopment Analysis (DEA), which is observed in practical applications such as public services and production processes. In the management context, the cost allocation problem tries to balance the different desires of two management layers: central manager and each sector manager. The cost can be assigned in an equitable way to the various Decision Making Units (DMUs). To achieve this goal, we present a new DEA-based method for dividing a fixed cost among DMUs. In the proposed method, the fixed cost is assigned to DMUs such that the efficiency measures and the Returns to Scale classifications of all DMUs before and after assigning the fixed cost remain unchanged. Also, the gaps among the costs allocated to DMUs will be minimized. The proposed method has the flexibility to consider the management standpoints. Finally, numerical results of an elucidatory example are furnished to demonstrate the applicability and reliability of our scheme.     

Identifying the anchor points in DEA using sensitivity analysis in linear programming

In this paper, the anchor points in DEA, as an important subset of the set of extreme efficient points of the production possibility set (PPS), are studied. A basic definition, utilizing the multiplier DEA models, is given. Then, two theorems are proved which provide necessary and sufficient conditions for characterization of these points. The main results of the paper lead to a new interesting connection between DEA and sensitivity analysis in linear programming theory. By utilizing the established theoretical results, a successful procedure for identification of the anchor points is presented.