Uncertainty in Data Envelopment Analysis
Fuzzy and Belief Degree-Based Uncertainties

An introduction to new methods of dealing with uncertain data in DEA models

Farhad Hosseinzadeh Lotfi (Author), Masoud Sanei (Author), Ali Asghar Hosseinzadeh (Author), Sadegh Niroomand (Author), Ali Mahmoodirad (Author)

9780323994446, Elsevier Science

Paperback / softback, published 24 May 2023

346 pages
22.9 x 15.2 x 2.2 cm, 0.45 kg

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.

1. Uncertain Theories
2. Introduction to Data Envelopment Analysis
3. Fuzzy Data Envelopment Analysis
4. Ranking and Sensitivity and Stability in Fuzzy DEA
5. Uncertain Data Envelopment Analysis
6. Ranking and Sensitivity and Stability in Uncertain DEA

Subject Areas: Artificial intelligence [UYQ]