Emrouznejad, A. and M. Tavana (2014). Performance Measurement with Fuzzy Data Envelopment Analysis. In the series of “Studies in Fuzziness and Soft Computing”, Springer-Verlag, ISBN 978-3-642-41371-1.
Since its introduction in 1978, Data Envelopment Analysis (DEA) has become one of the preeminent non-parametric methods for measuring efficiency and productivity of decision making units. DEA models are now employed routinely in areas that range from assessment of public sectors such as hospitals and healthcare systems, schools and universities to private sectors such as banks and financial institutions. The advantage of DEA is to accommodate multiple inputs and multiple outputs for measuring the relative efficiencies of a set of homogeneous decision making units (DMUs).
One limitation of the conventional DEA models is that they can only handle crisp input and output data. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. The aim of this book is to study various fuzzy methods for dealing with the imprecise and ambiguous data in DEA. This monograph is the first in fuzzy DEA (FDEA). It contains both the authors’ research work on fuzzy DEA and other developments, especially in the last 10 years, and it is a good indication of the outgrowth of the field of fuzzy data envelopment analysis.
With the exception of some basic notions in DEA and fuzzy theory, the book is completely self-contained. Important concepts in fuzziness and measuring efficiency are carefully motivated and introduced. Specifically, we have excluded any technical material that does not contribute directly to the understanding of fuzzy or DEA. Many other excellent textbooks are available today that discuss DEA in much more technical detail than is provided here. This book is aimed at upperlevel undergraduate as well as beginning graduate students who want to learn more about fuzziness in DEA or who are pursuing research in fuzzy DEA and related areas.
The main objective of this book is to provide the necessary background to work with existing fuzzy DEA models. Once the material in this book has been mastered, the reader will be able to apply fuzzy DEA models to his or her problems for measuring comparative efficiency of decision making units with imprecise data.