Within data envelopment analysis is a subgroup of papers in which many researchers have sought to improve the differential capabilities of DEA and to fully rank the decision making units (DMU). However, whilst each technique is useful in a specialist area, no one methodology can be prescribed as the complete solution to the question of ranking. Different results which are reached in applying th...

This paper addresses the problem of assigning shares of a new total fixed output to a group of decision making units (DMUs) using data envelopment analysis (DEA), by assuming the existence of a predefined hyperbolic locus of points that characterizes the DEA frontier. The problem of redistributing an already existing output is then addressed, where the total value of this output may vary, so th...

In the standard framework of data envelopment analysis (DEA) models, the returns to scale are fully characterized using the multiplier on the convexity constraint of inefficient decision making units (DMU) using the projection of the input-output vector on the frontier. In this note, we investigate how the returns to scale measurements in DEA models are affected by the presence of regulatory co...

In this study, both optimistic and pessimistic approaches of data envelopment analysis are applied to propose an equitable ranking method in fuzzy environments. To this end, we suppose that the sum of efficiency scores of all decision making units (DMUs) equals to unity. Using the worst-best and best-worst approaches, the minimum and maximum possible efficiency scores of each DMU are estimated ...

Data envelopment analysis (DEA) is known as a useful tool that produces many efficient decision-making units (DMUs). Traditional DEA provides relative efficient scores and reference sets, but does not influence and rank the efficient DMUs. This paper suggests a method that pro‐ vides influence and ranking information by using Pag‐ eRank as a centrality of Social Network analysis (SNA) based on ...

The traditional data envelopment analysis (DEA) model can evaluate the relative efficiencies of a set of decision making units (DMUs) with ratio scale inputs and outputs, but it cannot handle interval scale data. This study develop an approach to efficiency analysis to deal with both interval-scale data and ratio scale data. This approach introduces a measure of inefficiency and identifies effi...

Technique of Data Envelopment Analysis (DEA) involves methods conducted for desirable objective management of Decision Making Unit (DMU) that is same increasing of efficiency level. Data envelopment analysis furthermore determines the efficiency level, provides situation, removes inefficiency with evaluated benchmarking information. In this paper the use of the improvement Least-Distance measur...

Sensitivity analysis in data envelopment analysis (DEA) is studied for the case of the proportionate change of a subset of outputs or/and of a subset of inputs of an efficient decision making unit (DMU) according to the Charnes–Cooper–Rhodes (CCR) ratio model. Sufficient conditions for an efficient DMU to preserve its efficiency under the proportionate change of a subset of inputs or/and of out...

This research identifies returns to scale (RTS) of efficient decision making units (DMUs) with desirable (good) and undesirable (bad) inputs and outputs by presenting a new DEA (data envelopment analysis) approach. In this study, we first introduce a new input-output oriented model to determine efficient DMUs in the presence of undesirable factors and then, returns to scale of these DMUs are es...

This paper addresses issue of sensitivity of efficiency classification of variable returns to scale (VRS) technology for enhancing the credibility of data envelopment analysis (DEA) results in practical applications when an additional decision making unit (DMU) needs to be added to the set being considered. It also develops a structured approach to assisting practitioners in making an appropria...