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Sinopsis
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.
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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.
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.
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.
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.
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.
De la contraportada
Classical data envelopment analysis (DEA) models use crisp data in order to measure 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: Fuzzy and Belief Degree-Based Uncertainties 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 inputs and outputs of systems and processes are volatile and complex, making measurement difficult.
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Uncertainty in Data Envelopment Analysis: Fuzzy and Belief Degree-Based Uncertainties (Uncertainty, Computational Techniques, and Decision Intelligence)
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Uncertainty in Data Envelopment Analysis: Fuzzy and Belief Degree-Based Uncertainties (Uncertainty, Computational Techniques, and Decision Intelligence)
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Uncertainty in Data Envelopment Analysis: Fuzzy and Belief Degree-Based Uncertainties
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Uncertainty in Data Envelopment Analysis: Fuzzy and Belief Degree-Based Uncertainties (Uncertainty, Computational Techniques, and Decision Intelligence)
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