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Textbook:Data Envelopment Analysis with GAMS: A Handbook on Productivity Analysis and Performance Measurement
Author(s): Ali Emrouznejad
Course description:
The coursebook is organised into eight chapters. It begins with an introduction to the GAMS environment, with a focus on how to build a simple model, compile it, and derive the results. More specifically, Chap. 1 covers the syntax of the GAMS formulation, the import of data, loop structures, and the presentation of the results.
Chapter 2 lays the background to the DEA approach. Both envelopment and multiplier models are introduced, and these are discussed in terms of the type of frontier scale they use (i.e., CRS or VRS). Assurance regions and weight restrictions are addressed next. The topic of how resources should be allocated and used to achieve the best outcomes is of particular interest, as evidenced by the subsection on the most productive scale size. Finally, the chapter discusses super-efficiency models as an approach to dealing with situations in which there are multiple efficient units, and one needs to discriminate between them.
Chapter 3 focuses on extensions of DEA models, demonstrating recent developments in DEA formulations. The models analytically described and modelled with GAMS in this chapter concern DEA models with exogenously fixed variables and categorical variables, DEA models for handling desirable and undesirable outputs, congestion, and chance constraints.
Chapter 4 is dedicated to addressing non-radial DEA models. First, the chapter looks into CRS DEA and VRS DEA and their respective GAMS formulations. Then, the range-adjusted measure (RAM) is introduced, along with its CRS and VRS variants. The chapter further addresses the issue of negative data in DEA and presents three different approaches for dealing with such data.
In the context of DEA formulations, several measures have been proposed over the years for measuring the efficiency of DMUs. Chapter 5 delves into exploring allocative, cost, technical, revenue, and profit efficiency, along with their GAMS formulations.
Chapter 6 focuses on special cases in DEA. More specifically, the benefit-of-thedoubt approach (widely used in the development of composite indicators) is addressed first. This is then followed by a discussion of the multi-objective DEA models.
Chapter 7 addresses the topic of productivity change over a given period of time, with a focus on the Malmquist Productivity Index and the Malmquist–Luenberger Productivity Index.
Lastly, concluding remarks and direction for future work are presented in Chap. 8.
The chapters composing this book should be of considerable interest and provide our readers with useful information for their studies and research. We wish you informative reading!
Data Envelopment Analysis with GAMS_ A Handbook on Productivity Analysis.pdf
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