Fuzzy data envelopment analysis in the presence of undesirable outputs with ideal points

Developing a Data Envelopment Analysis Methodology for Supplier Selection in the Presence of Fuzzy Undesirable Factors

Development of Data Envelopment Analysis for the Performance Evaluation of Green Supply Chain with Undesirable Outputs

A fundamental problem is the use of DEA in multistep or multilevel processes such as supply chain, lack of attention to processes’ internal communications in a way that the recent studies on DEA in the context of serial processes have focused on closed systems that the outputs of one level become the inputs of the next level and none of the inputs enter the mediator process. The present study a...

Modeling Undesirable Outputs in Data Envelopment Analysis: Various Approaches

The general practice in performance and production efficiency measurement has been to ignore additional products of most transformation processes that can be classified as " undesirable outputs " — which are a subset of the output set. Without the inclusion of these factors, the efficiency evaluation becomes a purely technical measure of the system alone, and does not account for the interactio...

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...

Ranking DMUs by ideal points in the presence of fuzzy and ordinal data

Envelopment Analysis (DEA) is a very eective method to evaluate the relative eciency of decision-making units (DMUs). DEA models divided all DMUs in two categories: ecient and inecientDMUs, and don't able to discriminant between ecient DMUs. On the other hand, the observedvalues of the input and output data in real-life problems are sometimes imprecise or vague, suchas interval data, ordinal da...