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Uncertainty in Data Envelopment Analysis
Fuzzy and Belief Degree-Based Uncertainties
1st Edition - May 19, 2023
Authors: Farhad Hosseinzadeh Lotfi, Masoud Sanei, Ali Asghar Hosseinzadeh, Sadegh Niroomand, Ali Mahmoodirad
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 4 4 4 - 6
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 4 4 5 - 3
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,… Read more
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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.
- 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
Graduate students, researchers, and professional engineers who study or perform optimization and evaluation, in the fields of applied mathematics, industrial engineering, computer science, information science, management science, economics, and operations research
- Cover image
- Title page
- Table of Contents
- Front Matter
- Copyright
- Preface
- Chapter One: Uncertain theories
- Abstract
- 1.1: Introduction
- 1.2: Fuzzy sets theory
- 1.3: Belief degree-based uncertainty theory
- References
- Further reading
- Chapter Two: Introduction to data envelopment analysis
- Abstract
- 2.1: Introduction
- 2.2: Basic definitions
- 2.3: The DEA models based on production possibility set
- 2.4: Nonincreasing and nondecreasing returns to scale models
- 2.5: Nonradial DEA models
- 2.6: Stability of DEA models for unit of scale change and transmission
- 2.7: Cost and revenue efficiencies
- 2.8: Weight restrictions
- References
- Chapter Three: Fuzzy data envelopment analysis
- Abstract
- 3.1: Introduction
- 3.2: Fuzzy production possibility set (FPPS)
- 3.3: Fuzzy environment in DEA
- 3.4: Solution approaches of the fuzzy DEA models
- 3.5: The fuzzy additive DEA model
- 3.6: The fuzzy SBM model
- References
- Chapter Four: Ranking, sensitivity and stability analysis in fuzzy DEA
- Abstract
- 4.1: Introduction
- 4.2: Ranking models in fuzzy DEA
- 4.3: Sensitivity analysis and stability of fuzzy DEA models
- References
- Chapter Five: Uncertain data envelopment analysis
- Abstract
- 5.1: Introduction
- 5.2: Deterministic PPS
- 5.3: Identification function
- 5.4: Uncertain PPS (UPPS)
- 5.5: Belief degree-based uncertain DEA models
- 5.6: Uncertain input-oriented CCR envelopment model
- 5.7: Uncertain input-oriented CCR model with multiplier form
- 5.8: Uncertain DEA model for scale efficiency evaluation
- 5.9: Uncertain DEA model for special scale efficiency
- 5.10: Uncertain BCC models
- 5.11: Uncertain additive model
- 5.12: Uncertain SBM model
- 5.13: Russel uncertainty model
- 5.14: Uncertain cost and revenue DEA model
- References
- Further reading
- Chapter Six: Ranking, sensitivity, and stability analysis in uncertain DEA
- Abstract
- 6.1: Introduction
- 6.2: Uncertain superefficiency model
- 6.3: Uncertain modified MAJ model
- 6.4: Sensitivity and stability analysis of the additive model
- 6.5: Analysis and stability of uncertain model (5.20)
- 6.6: Analysis and stability of model (5.42)
- 6.7: A model for obtaining maximum possible belief degree for an efficient DMU
- References
- Index
- No. of pages: 346
- Language: English
- Published: May 19, 2023
- Imprint: Academic Press
- Paperback ISBN: 9780323994446
- eBook ISBN: 9780323994453
FL
Farhad Hosseinzadeh Lotfi
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.
Affiliations and expertise
Professor of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, IranMS
Masoud Sanei
Masoud Sanei is an Associate Professor at the Department of Applied Mathematics, Islamic Azad University, Central Tehran Branch, in Iran. His research interests are in the areas of operation research such as Data Envelopment Analysis, Uncertainty Theory, and Supply Chain Management. He has several papers in journals and conference proceedings.
Affiliations and expertise
Associate Professor of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran, IranAH
Ali Asghar Hosseinzadeh
Ali Asghar Hosseinzadeh is an Assistant Professor of Applied Mathematics in the Lahijan branch of Islamic Azad University, in Iran. His research interests include Fuzzy Mathematical Programming, Data Envelopment Analysis, and Uncertainty Theory. He has published research articles in international journals of Mathematics and Industrial Engineering.
Affiliations and expertise
Assistant Professor of Mathematics, Lahijan Branch, Islamic Azad University, Lahijan, IranSN
Sadegh Niroomand
Sadegh Niroomand is an Associate Professor of Industrial Engineering in Firouzabad Institute of Higher Education, in Iran. He received his PhD degree in Industrial Engineering from Eastern Mediterranean University. His research interests are Operations Research, Fuzzy Theory, Exact and Meta-heuristic Solution Approaches.
Affiliations and expertise
Associate Professor of Industrial Engineering, Firouzabad Institute of Higher Education, Firouzabad, Fars, IranAM
Ali Mahmoodirad
Ali Mahmoodirad is an Associate Professor of Applied Mathematics in Masjed-Sleiman branch of Islamic Azad University in Iran. His research interests include Fuzzy Mathematical Programming, Supply Chain Management, and Uncertainty Theory. He has published research articles in international journals of Mathematics and industrial engineering.
Affiliations and expertise
Associate Professor of Applied Mathematics, Masjed-Soleiman Branch Islamic Azad University, Masjed-Soleiman, Iran