This book provides a comprehensive and practical introduction to Data Envelopment Analysis (DEA). It explains how this non-parametric technique is used to measure performance and extract efficiency from homogeneous entities within a production procedure. It situates DEA within a growing field of productivity analysis and performance measurement, for which numerous models have been proposed.
This book encapsulates all of the advances in DEA models proposed in the literature. These models are presented in the context of the GAMS software, which is a powerful tool for mathematical programming models. This book serves two educational purposes: it introduces readers to DEA models and provides examples using GAMS. In addition, the reader is introduced to GAMS programming, as well as innovative and practical applications. GAMS codes are available for free, allowing readers to test and expand the models to meet their specific needs.
Author(s): Ali Emrouznejad, Konstantinos Petridis, Vincent Charles
Series: International Series in Operations Research & Management Science, 338
Publisher: Springer
Year: 2023
Language: English
Pages: 159
City: Cham
Preface
Contents
About the Author
Chapter 1: Introduction to GAMS
1.1 Introduction
1.2 The Syntax of the GAMS Model
1.2.1 An Illustrative Example
1.2.2 Compiling the Model
1.2.3 Analysing the Results
1.3 Importing Data into GAMS
1.4 Loops in GAMS
1.4.1 Presenting the Results
Chapter 2: Introduction to Data Envelopment Analysis
2.1 Introduction
2.2 Envelopment Models
2.2.1 Illustrative Examples
2.2.2 GAMS Implementation
2.2.3 The CRS DEA Model
2.2.4 The VRS DEA Model
2.2.5 Projected Values and Targets
2.3 Multiplier Models
2.3.1 The CRS DEA Model
2.3.2 The VRS DEA Model
2.4 Assurance Regions/Weight Restrictions
2.5 Most Productive Scale Size
2.6 Super-Efficiency Models
Chapter 3: Extensions of DEA Models
3.1 Introduction
3.2 Exogenously Fixed Variables
3.2.1 DEA CRS Model
3.2.2 DEA VRS Model
3.3 Undesirable Outputs
3.4 Congestion in DEA
3.4.1 Congestion Index
3.4.2 Congestion with Slack Variables
3.5 Categorical Variables in DEA
3.6 Chance-Constrained DEA Model
3.6.1 The Modelling of Chance Constraints
3.6.2 Stochastic Efficiency in Marginal Chance-Constrained Models
Chapter 4: Non-radial DEA Models
4.1 Introduction
4.2 Non-radial CRS DEA
4.3 Non-radial VRS DEA
4.4 Range-Adjusted Measure Model
4.4.1 RAM CRS DEA Model
4.4.2 RAM VRS DEA Model
4.5 Negative Data in DEA
4.5.1 The Range Directional Model
4.5.2 The Modified Slacks-Based Model
4.5.3 The Semi-Oriented Radial Measure
Chapter 5: Allocative, Cost, Technical, Revenue, and Profit Efficiency
5.1 Introduction
5.2 Allocative and Cost Efficiency
5.2.1 Data for Cost Efficiency
5.2.2 GAMS Formulation for Allocative and Cost Efficiency
5.3 Revenue and Technical Efficiency
5.3.1 Data for Revenue Efficiency
5.3.2 GAMS Formulation for Revenue Efficiency
5.4 Profit Efficiency
5.4.1 GAMS Formulation for Profit Efficiency
Chapter 6: Special Cases in DEA
6.1 Introduction
6.2 Benefit-of-the-Doubt
6.2.1 Data for the BoD Example
6.2.2 GAMS Formulation for BoD
6.3 Multi-objective Linear Programming in DEA
6.3.1 Data for MOLP in DEA
6.3.2 Scenarios for Weights for MOLP in DEA
6.3.3 GAMS Formulation for MOLP in DEA
Chapter 7: Productivity Change
7.1 Introduction
7.2 Calculation of the Malmquist Productivity Index
7.2.1 Data for the Calculation of MPI
7.2.2 GAMS Formulation for MPI
7.3 Calculation of the Malmquist-Luenberger Productivity Index
7.3.1 Data for the Calculation of MLPI
7.3.2 GAMS Formulation for MLPI
Chapter 8: Concluding Remarks
8.1 Introduction
8.2 General Algebraic Modelling System
8.3 Data Envelopment Analysis Models
8.3.1 Conventional DEA
8.3.2 Productivity Change
8.4 Data Envelopment Analysis Software
8.5 GAMS for Data Envelopment Analysis
8.6 Conclusions and Future Developments
References
