Data Envelopment Analysis Models in Non-Homogeneous Environment

Data Envelopment Analysis Models In The Presence Of Ratio Data and Non-Discretionary Factors

Generalized Fuzzy Inverse Data envelopment Analysis Models

Traditional DEA models do not deal with imprecise data and assume that the data for all inputs and outputs are known exactly. Inverse DEA models can be used to estimate inputs for a DMU when some or all outputs and efficiency level of this DMU are increased or preserved. this paper studies the inverse DEA for fuzzy data. This paper proposes generalized inverse DEA in fuzzy data envelopment anal...

Flexibility of Variations in Radial and Non-Radial Data Envelopment Analysis ‎Models

One of the major problems in Data Envelopment Analysis (DEA) is to determine the projection of inefficient Decision Making Units (DMUs) into the efficient frontier. In conventional DEA models, inputs and outputs of inefficient DMUs alter arbitrarily for reaching to the efficient frontier. Nevertheless, sometimes the ability of DMUs is defined and restricted. Moreover, there are situations in th...

Efficiency of Centralized Structures in Data Envelopment Analysis Ratio Models

This paper investigates the centralized resource allocation with centralized structures by using the data envelopment analysis-ratio (DEA-R) models. To this end, it proposes a method to determine the resource allocation of centralized structures such that the ratio of inputs to outputs are minimized.

Non-discretionary Factors in Data Envelopment Analysis: Review and Extension