Data Envelopment Analysis (DEA) is a great approach used for measuring relative efficiencies and performance of a collection of Decision Making Units (DMUs). These used in the various forms, such as hospitals, universities, air force, banks, courts, business firms, and others, including the performance of countries, regions, etc. One of the earliest applications of DEA called Education. It was ...

This research uses a new analytical approach for baseball evaluation, referred to as "A Bench Mark Approach," [1] by combining DEA (Data Envelopment Analysis) with OERA (Offensive Earned-Run Average). An important feature of the approach is that it can select a best performer among many baseball players and evaluate their ranking scores. The DEA is an evaluation technique of various entities in...

Statement of Scope and Purpose. This is a comprehensive study of preprocessing in DEA. The purpose is to provide tools that will reduce the computational burden of DEA studies especially in large scale applications. Abstract. We collect, organize, analyze, implement, test, and compare a comprehensive list of ideas for pre-processors for entity classification in DEA. We limit our focus to proced...

Intensification of swine production in the U.S. and around the world requires advanced manure management. For swine manure management in the state of North Carolina, one system met all of the required advanced management criteria, and it was qualified as an environmentally superior technology. This investigation was part of the testing for this superior technology. The objectives of this invest...

This paper is aimed to give an illustration on the application of Data Envelopment Analysis (DEA) as a tool to assess Quality Management (QM) efficiency. A variant of DEA, slack based measure (SBM) is used for this purpose. From this study, it is found that DEA is suitable to measure QM efficiency and give improvement suggestions to the inefficient QM. Keywords—Quality Management, Data Envelopm...

This paper considers the relationship between Tolerance sensitivity analysis in optimization and metric sensitivity analysis in Data Envelopment Analysis (DEA). Herein, we extend the results on the generalized Tolerance framework proposed by Wendell and Chen and show how this framework includes DEA metric sensitivity as a special case. Further, we note how recent results in Tolerance sensitivit...

Operator allocation is one of the most important decisions that can influence productivity in the laborintensive manufacturing system. In this paper 10 operator allocation alternatives are identified with the assistance of computer simulation. To determine the best operator allocation, AHP/DEA and DEA Cross Efficiency are used. The results of both techniques are compared. Based on the results, ...

We survey the methodological advances in DEA over the last 25 years and discuss the necessary conditions for a sound empirical application. We hope this survey will contribute to the further dissemination of DEA, the knowledge of its relative strengths and weaknesses, and the tools currently available for exploiting its full potential. Our main points are illustrated by the case of the DEA stud...

In this paper, we re-examine Data Envelopment Analysis models from perspectives of preference order, production set and performance measure. we investigate the relationship between Data Envelopment Analysis (DEA) and Multiple Criteria Decision Making Theory. There are three key building blocks in a DEA model: preference order, production possibility set and performance measure. It is shown in t...

Data envelopment analysis (DEA) is a non-parametric approach for measuring the relative efficiencies of peer decision making units (DMUs). Many studies have examined DEA efficiencies of two-stage systems, where all the outputs from the first stage are the only inputs to the second stage. Although single-stage DEA models with undesirable input-outputs have been extensively studied, there still l...