Uncertainty in Data Envelopment Analysis (ePub)
Fuzzy and Belief Degree-Based Uncertainties
(Sprache: Englisch)
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...
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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.
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
Autoren-Porträt von Farhad Hosseinzadeh Lotfi, Masoud Sanei, Ali Asghar Hosseinzadeh, Sadegh Niroomand, Ali Mahmoodirad
Dr. Lotfi is a Full Professor of Mathematics at the Science and Research Branch, Islamic Azad University (IAU), Tehran, Iran. In 1992, he received his undergraduate degree in Mathematics at Yazd University, Yazd, Iran. He received his M.Sc in Operations Research at IAU, Lahijan, Iran in 1996 and PhD in Applied Mathematics (O.R.) at IAU, Science and Research Branch, Tehran, Iran in 2000. His major research interests are operations research and data envelopment analysis. He has published more than 300 scientific and technical papers in leading scientific journals, including European Journal of Operational Research, Computers and Industrial Engineering, Journal of the Operational Research Society, Applied Mathematics and Computation, Applied Mathematical Modelling, Mathematical and Computer Modelling, and Journal of the Operational Research Society of Japan, etc. He is Editor-in-Chief and member of editorial board of Journal of Data Envelopment Analysis and Decision Science. He is also Director-in-Charge and member of editorial board of International Journal of Industrial Mathematics.
Bibliographische Angaben
- Autoren: Farhad Hosseinzadeh Lotfi , Masoud Sanei , Ali Asghar Hosseinzadeh , Sadegh Niroomand , Ali Mahmoodirad
- 2023, 346 Seiten, Englisch
- Verlag: Elsevier Science & Techn.
- ISBN-10: 0323994458
- ISBN-13: 9780323994453
- Erscheinungsdatum: 01.06.2023
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- Dateiformat: ePub
- Größe: 7.85 MB
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Englisch
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