Uncertain Data Envelopment Analysis
Download Uncertain Data Envelopment Analysis PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Uncertain Data Envelopment Analysis book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Uncertain Data Envelopment Analysis
This book is intended to present the milestones in the progression of uncertain Data envelopment analysis (DEA). Chapter 1 gives some basic introduction to uncertain theories, including probability theory, credibility theory, uncertainty theory and chance theory. Chapter 2 presents a comprehensive review and discussion of basic DEA models. The stochastic DEA is introduced in Chapter 3, in which the inputs and outputs are assumed to be random variables. To obtain the probability distribution of a random variable, a lot of samples are needed to apply the statistics inference approach. Chapter 4 and 5 provide two uncertain DEA methods to evaluate the DMUs with limited or insufficient statistical data, named fuzzy DEA and uncertain DEA. In order to evaluate the DMUs in which uncertainty and randomness appear simultaneously, the hybrid DEA based on chance theory is presented in Chapter 6.
Uncertainty in Data Envelopment Analysis
Author: Farhad Hosseinzadeh Lotfi
language: en
Publisher: Elsevier
Release Date: 2023-05-19
Classical data envelopment analysis (DEA) models use crisp data to measure the inputs and outputs of a given system. In cases such as manufacturing systems, production processes, service systems, etc., the inputs and outputs may be complex and difficult to measure with classical DEA models. Crisp input and output data are fundamentally indispensable in the conventional DEA models. If these models contain complex uncertain data, then they will become more important and practical for decision makers.Uncertainty in Data Envelopment Analysis introduces methods to investigate uncertain data in DEA models, providing a deeper look into two types of uncertain DEA methods, fuzzy DEA and belief degree-based uncertainty DEA, which are based on uncertain measures. These models aim to solve problems encountered by classical data analysis in cases where the inputs and outputs of systems and processes are volatile and complex, making measurement difficult. - Introduces methods to deal with uncertain data in DEA models, as a source of information and a reference book for researchers and engineers - Presents DEA models that can be used for evaluating the outputs of many reallife systems in social and engineering subjects - Provides fresh DEA models for efficiency evaluation from the perspective of imprecise data - Applies the fuzzy set and uncertainty theories to DEA to produce a new method of dealing with the empirical data
Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis
Author: Joe Zhu
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-06-08
In a relatively short period of time, data envelopment analysis (DEA) has grown into a powerful analytical tool for measuring and evaluating performance. DEA is computational at its core and this book is one of several Springer aim to publish on the subject. This work deals with the micro aspects of handling and modeling data issues in DEA problems. It is a handbook treatment dealing with specific data problems, including imprecise data and undesirable outputs.