Data Envelopment

Introduction to the Theory and Application of Data Envelopment Analysis: A Foundation Text with Integrated Software

Introduction to the Theory and Application of Data Envelopment Analysis: A Foundation Text with Integrated Software by Emmanuel Thanassoulis
English | PDF | 2001 | 296 Pages | ISBN : 0792374290 | 23.3 MB

1 DATA ENVELOPMENT ANALYSIS Data Envelopment Analysis (DEA) was initially developed as a method for assessing the comparative efficiencies of organisational units such as the branches of a bank, schools, hospital departments or restaurants. The key in each case is that they perform feature which makes the units comparable the same function in terms of the kinds of resource they use and the types of output they produce. For example all bank branches to be compared would typically use staff and capital assets to effect income generating activities such as advancing loans, selling financial products and carrying out banking transactions on behalf of their clients. The efficiencies assessed in this context by DEA are intended to reflect the scope for resource conservation at the unit being assessed without detriment to its outputs, or alternatively, the scope for output augmentation without additional resources. The efficiencies assessed are comparative or relative because they reflect scope for resource conservation or output augmentation at one unit relative to other comparable benchmark units rather than in some absolute sense. We resort to relative rather than absolute efficiencies because in most practical contexts we lack sufficient information to derive the superior measures of absolute efficiency. DEA was initiated by Charnes Cooper and Rhodes in 1978 in their seminal paper Chames et al. (1978). The paper operationalised and extended by means of linear programming production economics concepts of empirical efficiency put forth some twenty years earlier by Farrell (1957).

Data Envelopment Analysis: A Handbook of Models and Methods  eBooks & eLearning

Posted by step778 at July 23, 2020
Data Envelopment Analysis: A Handbook of Models and Methods

Joe Zhu, "Data Envelopment Analysis: A Handbook of Models and Methods"
English | 2015 | pages: 472 | ISBN: 1489975527 | PDF | 5,2 mb

Data Envelopment Analysis and Its Applications to Management  eBooks & eLearning

Posted by DZ123 at Nov. 14, 2022
Data Envelopment Analysis and Its Applications to Management

Vincent Charles, Mukesh Kumar, "Data Envelopment Analysis and Its Applications to Management"
English | 2012 | ISBN: 1443841323 | PDF | pages: 286 | 1.6 mb

Network Data Envelopment Analysis: Foundations and Extensions (2nd Edition)  eBooks & eLearning

Posted by hill0 at July 25, 2023
Network Data Envelopment Analysis: Foundations and Extensions (2nd Edition)

Network Data Envelopment Analysis: Foundations and Extensions
English | 2023 | ISBN: 3031275926 | 746 Pages | PDF EPUB (True) | 39 MB

Introduction and Comparison of Data Envelopment Analysis Software Packages  eBooks & eLearning

Posted by hill0 at April 27, 2021
Introduction and Comparison of Data Envelopment Analysis Software Packages

Introduction and Comparison of Data Envelopment Analysis Software Packages
by Alireza Alinezhad

English | 2021 | ISBN: 1536191191 | 324 Pages | PDF | 18 MB

Data Envelopment Analysis with GAMS  eBooks & eLearning

Posted by hill0 at July 5, 2023
Data Envelopment Analysis with GAMS

Data Envelopment Analysis with GAMS: A Handbook on Productivity Analysis and Performance Measurement
English | 2023 | ISBN: 3031307003 | 212 Pages | PDF EPUB (True) | 29 MB

Data Envelopment Analysis with R  eBooks & eLearning

Posted by AvaxGenius at Nov. 30, 2020
Data Envelopment Analysis with R

Data Envelopment Analysis with R by Farhad Hosseinzadeh Lotfi
English | EPUB | 2020 | 248 Pages | ISBN : 3030242765 | 8.6 MB

This book introduces readers to the use of R codes for optimization problems. First, it provides the necessary background to understand data envelopment analysis (DEA), with a special emphasis on fuzzy DEA. It then describes DEA models, including fuzzy DEA models, and shows how to use them to solve optimization problems with R. Further, it discusses the main advantages of R in optimization problems, and provides R codes based on real-world data sets throughout. Offering a comprehensive review of DEA and fuzzy DEA models and the corresponding R codes, this practice-oriented reference guide is intended for masters and Ph.D. students in various disciplines, as well as practitioners and researchers.

Comparative Efficiency in Data Envelopment Analysis Based on Ratio Analysis  eBooks & eLearning

Posted by hill0 at Oct. 1, 2023
Comparative Efficiency in Data Envelopment Analysis Based on Ratio Analysis

Comparative Efficiency in Data Envelopment Analysis Based on Ratio Analysis
English | 2023 | ISBN: 3031431804 | 289 Pages | PDF EPUB (True) | 19 MB

Analytical Decision Making and Data Envelopment Analysis  eBooks & eLearning

Posted by hill0 at Oct. 30, 2024
Analytical Decision Making and Data Envelopment Analysis

Analytical Decision Making and Data Envelopment Analysis: Advances and Challenges
English | 2024 | ISBN: 981976971X | 492 Pages | PDF EPUB (True) | 55 MB

Handbook of Massive Data Sets  eBooks & eLearning

Posted by AvaxGenius at June 21, 2022
Handbook of Massive Data Sets

Handbook of Massive Data Sets by James Abello
English | PDF | 2002 | 1209 Pages | ISBN : 1402004893 | 132.2 MB

The proliferation of massive data sets brings with it a series of special computational challenges. This "data avalanche" arises in a wide range of scientific and commercial applications. With advances in computer and information technologies, many of these challenges are beginning to be addressed by diverse inter-disciplinary groups, that indude computer scientists, mathematicians, statisticians and engineers, working in dose cooperation with application domain experts. High profile applications indude astrophysics, bio-technology, demographics, finance, geographi­ cal information systems, government, medicine, telecommunications, the environment and the internet. John R. Tucker of the Board on Mathe­ matical Seiences has stated: "My interest in this problern (Massive Data Sets) isthat I see it as the rnost irnportant cross-cutting problern for the rnathernatical sciences in practical problern solving for the next decade, because it is so pervasive.