{"id":20065,"date":"2016-01-07T20:29:36","date_gmt":"2016-01-07T20:29:36","guid":{"rendered":"http:\/\/deazone.com\/en\/?p=20065"},"modified":"2016-01-07T20:35:21","modified_gmt":"2016-01-07T20:35:21","slug":"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-97","status":"publish","type":"post","link":"https:\/\/deazone.com\/en\/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-97","title":{"rendered":"Emrouznejad, A. and M. Tavana (2014). Performance Measurement with Fuzzy Data Envelopment Analysis. In the series of \u201cStudies in Fuzziness and Soft Computing\u201d, Springer-Verlag, ISBN 978-3-642-41371-1."},"content":{"rendered":"

Emrouznejad, A.<\/u><\/strong> and M. Tavana (2014). Performance Measurement with Fuzzy Data Envelopment Analysis<\/em>. In the series of \u201cStudies in Fuzziness and Soft Computing\u201d, Springer-Verlag, ISBN 978-3-642-41371-1.<\/p>\n

Since its introduction in 1978, Data Envelopment Analysis (DEA) has become one\u00a0of the preeminent non-parametric methods for measuring efficiency and productivity\u00a0of decision making units. DEA models are now employed routinely in areas\u00a0that range from assessment of public sectors such as hospitals and healthcare\u00a0systems, schools and universities to private sectors such as banks and financial\u00a0institutions. The advantage of DEA is to accommodate multiple inputs and multiple\u00a0outputs for measuring the relative efficiencies of a set of homogeneous\u00a0decision making units (DMUs).
\nOne limitation of the conventional DEA models is that they can only handle\u00a0crisp input and output data. However, the observed values of the input and output\u00a0data in real-world problems are sometimes imprecise or vague. The aim of this\u00a0book is to study various fuzzy methods for dealing with the imprecise and\u00a0ambiguous data in DEA. This monograph is the first in fuzzy DEA (FDEA).\u00a0It contains both the authors\u2019 research work on fuzzy DEA and other developments,\u00a0especially in the last 10 years, and it is a good indication of the outgrowth of the\u00a0field of fuzzy data envelopment analysis.
\nWith the exception of some basic notions in DEA and fuzzy theory, the book is\u00a0completely self-contained. Important concepts in fuzziness and measuring efficiency\u00a0are carefully motivated and introduced. Specifically, we have excluded any\u00a0technical material that does not contribute directly to the understanding of fuzzy or\u00a0DEA. Many other excellent textbooks are available today that discuss DEA in\u00a0much more technical detail than is provided here. This book is aimed at upperlevel\u00a0undergraduate as well as beginning graduate students who want to learn more\u00a0about fuzziness in DEA or who are pursuing research in fuzzy DEA and related\u00a0areas.
\nThe main objective of this book is to provide the necessary background to work\u00a0with existing fuzzy DEA models. Once the material in this book has been mastered,\u00a0the reader will be able to apply fuzzy DEA models to his or her problems for\u00a0measuring comparative efficiency of decision making units with imprecise data.<\/p>\n

[ Download1\u00a0 <\/a>\u00a0or\u00a0\u00a0 Download2<\/a> ]<\/p>\n","protected":false},"excerpt":{"rendered":"

Emrouznejad, A. and M. Tavana (2014). Performance Measurement with Fuzzy Data Envelopment Analysis. In the series of \u201cStudies in Fuzziness and Soft Computing\u201d, Springer-Verlag, ISBN 978-3-642-41371-1. Since its introduction in 1978, Data Envelopment Analysis (DEA) has become one\u00a0of the preeminent non-parametric methods for measuring efficiency and productivity\u00a0of decision making units. DEA models are now employed … <\/p>\n

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