Uncertainty in data envelopment analysis : fuzzy and belief degree-based uncertainties /

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 in...

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Bibliographic Details
Main Authors: Hosseinzadeh Lotfi, Farhad, 1967- (Author), Sanei, Masoud (Author), Hosseinzadeh, Ali Asghar (Author), Niroomand, Sadegh (Author), Mahmoodirad, Ali (Author)
Corporate Author: ScienceDirect (Online service)
Format: eBook
Language:English
Published: London, United Kingdom ; San Diego, CA : Academic Press, [2023]
Series:Uncertainty, computational techniques, and decision intelligence
Subjects:
Online Access:Connect to the full text of this electronic book

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245 1 0 |a Uncertainty in data envelopment analysis :  |b fuzzy and belief degree-based uncertainties /  |c Farhad Hosseinzadeh Lotfi, Masoud Sanei, Ali Asghar Hosseinzadeh, Sadegh Niroomand, Ali Mahmoodirad. 
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490 0 |a Uncertainty, computational techniques, and decision intelligence 
504 |a Includes bibliographical references and index. 
588 |a Description based on online resource; title from digital title page (viewed on September 21, 2023). 
520 |a 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. 
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