In data envelopment analysis (DEA), performance evaluation is generally assumed to be based upon a set of quantitative data. In many real world settings, however, it is essential to take into account the presence of qualitative factors when evaluating the performance of decision making units (DMUs). Very often rankings are provided from best to worst relative to particular attributes. Such rank...

The topology of a network seriously affects its cost, reliability, throughput, and traffic pattern, etc, so we need to simultaneously consider these multiple criteria, which have different units, when evaluating network topologies. However, ordinary methods of network topology design considered only a single criterion. DEA (data envelopment analysis) enables us to simultaneously evaluate multip...

An objective measure of building energy performance is crucial for performance assessment and rational decision making on energy retrofits and policies of existing buildings. One of the most popular measures of building energy performance benchmarking is Energy Use Intensity (EUI, kwh/m2). While EUI is simple to understand, it only represents the amount of consumed energy per unit floor area ra...

This paper reports on an innovative combination of hard and soft methods—soft systems methodology (SSM) with data envelopment analysis (DEA). Problems in defining and agreeing appropriate inputs and outputs for DEA led to the use of SSM as a way or producing a comprehensive and systemic database of performance indicators. The contributions of the paper are: the use of SSM to improve DEA specifi...

Cross-efficiency evaluation is an effective approach to ranking decision making units (DMUs) that utilize multiple inputs to produce multiple outputs. Its models can usually be developed in a way that is either aggressive or benevolent to other DMUs, depending upon the decision maker (DM)’s subjective preference to the two extreme cases. This paper proposes several new data envelopment analysis...

Traditional DEA method is improper for supplier evaluation and selection, as it adopts varying weights in evaluation, and fails to consider competition among the suppliers. In order to solve these two problems, Nash bargaining game DEA model is applied to supplier evaluation in present paper. However, there is a non-uniqueness problem with Nash bargaining game efficiency of supplier in existing...

This paper examines the Data Envelopment Analysis (DEA) methodology from a cognitive perspective. Specifically, it analyzes (a) the role of DEA scores as an overall efficiency measure and (b) to what extent the presence of DEA scores for a non-financial performance appraisal influences a posterior financial performance assessment. The study confirms that the efficiency score acts as a strong pe...

Abstract: The radial measures of classical DEA models (CCR, BCC) are incomplete, they are only separate measures of input and output efficiency and their efficiency index omit the non-zero input and output slacks. Enhanced Russell graph measure (ERM) eliminates these deficiencies. All of the existing fuzzy DEA models are extension of CCR or BCC model, efficiencies of DMUs, ultimately, are solut...

Super-efficiency data envelopment analysis (DEA) model is obtained when a decision making unit (DMU) under evaluation is excluded from the reference set. Because of the possible infeasibility of super-efficiency DEA model, the use of super-efficiency DEA model has been restricted to the situations where constant returns to scale (CRS) are assumed. It is shown that one of the input-oriented and ...

In the data envelopment analysis (DEA) literature, linear fractional non-cooperative network DEA models for two-stage network structures are often transformed into parametric linear models. The transformed parametric linear models are then solved by computing a series of linear models when the parameter is varied. For example, Wu, Zhu, Ji, Chu and Liang (2016) provide a linear fractional non-co...