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

Uncertainty in Data Envelopment Analysis (DEA) models allows for the investigation of uncertain data, providing a deeper look into fuzzy DEA and belief degree-based uncertainty DEA methods. These models are useful in cases where classical DEA models are difficult to apply due to volatile and complex inputs and outputs.

Format: Paperback / softback
Length: 346 pages
Publication date: 24 May 2023
Publisher: Elsevier Science & Technology

Classical data envelopment analysis (DEA) models rely on precise data to evaluate the inputs and outputs of a system. However, in industries like manufacturing, production, and service systems, the inputs and outputs can be intricate and challenging to measure using traditional DEA models. The significance of crisp input and output data becomes paramount when dealing with complex and uncertain data. In such cases, these models become more valuable and practical for decision-makers.

Uncertainty in data envelopment analysis arises to address the challenges posed by uncertain data in DEA models. It introduces two types of uncertain DEA methods: fuzzy DEA and belief degree-based uncertainty DEA. These methods are based on uncertain measures, providing a deeper understanding of how to handle uncertain data within DEA frameworks.

The primary objective of these models is to address the problems encountered by classical data analysis in scenarios where the inputs and outputs of systems and processes are volatile and complex, making accurate measurement a daunting task. By incorporating uncertainty analysis, DEA models can provide a more comprehensive evaluation of performance, allowing decision-makers to make informed decisions based on a broader range of information.

In conclusion, classical data envelopment analysis models are enhanced by the incorporation of uncertainty analysis, enabling them to handle complex and uncertain data more effectively. This approach provides decision-makers with a deeper understanding of system performance and facilitates more informed decision-making processes in industries where volatility and complexity are prevalent.

Weight: 566g
Dimension: 150 x 229 x 19 (mm)
ISBN-13: 9780323994446