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

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

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.

A method to consider Non-Discretionary factors in Data Envelopment Analysis

The technique for efficiency measurement known as Data Envelopment Analysis (DEA) has been extended to allow on-discretionary inputs that affect production. Several methods exist for measuring efficiency to control these factors in production. This paper review these approaches, providing a discussion of strengths and weaknesses and highlighting potential limitations. In addition, a new approac...

A data envelopment analysis model with discretionary and non-discretionary factors in fuzzy environments

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision-making units (DMUs) that use multiple inputs to produce multiple outputs. The standard DEA models assume that all inputs and outputs are crisp and can be changed at the discretion of management. While crisp input and output data are fundamentally indispensable in the standard DEA evalua...

An extended of multiple criteria data envelopment analysis models for ratio data

One of the problems of the data envelopment analysis traditional models in the multiple form that is the weights corresponding to certain inputs and outputs are considered zero in the calculation of efficiency and this means that not all input and output components are utilized for the evaluation of efficiency, as some are ignored. The above issue causes the efficiency score of the under evalua...

Estimating Returns to Scale in the Presence of Undesirable Factors in Data Envelopment Analysis

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...