Fuzzy data envelopment analysis: A fuzzy expected value approach

Authors:
Ying-Ming Wang;Kwai-Sang Chin
Affiliations:
School of Public Administration, Fuzhou University, Fuzhou 350002, PR China;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 8
Cited 4

Fuzzy efficiency measures in data envelopment analysis

Fuzzy Sets and Systems
Data envelopment analysis with imprecise data: an application of Taiwan machinery firms

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
A fuzzy DEA/AR approach to the selection of flexible manufacturing systems

Computers and Industrial Engineering
Fuzzy efficiency measures in fuzzy DEA/AR with application to university libraries

Expert Systems with Applications: An International Journal
Fuzzy data envelopment analysis (DEA): Model and ranking method

Journal of Computational and Applied Mathematics
Fuzzy data envelopment analysis based upon fuzzy arithmetic with an application to performance assessment of manufacturing enterprises

Expert Systems with Applications: An International Journal
Interval efficiency assessment using data envelopment analysis

Fuzzy Sets and Systems
Expected value of fuzzy variable and fuzzy expected value models

IEEE Transactions on Fuzzy Systems

A concept of fuzzy input mix-efficiency in fuzzy DEA and its application in banking sector

Expert Systems with Applications: An International Journal
Chance-constrained DEA models with random fuzzy inputs and outputs

Knowledge-Based Systems
Fuzzy rough DEA model: A possibility and expected value approaches

Expert Systems with Applications: An International Journal
A hybrid particle swarm optimization algorithm for satisficing data envelopment analysis under fuzzy chance constraints

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.06

Visualization

Abstract

Performance assessment often has to be conducted under uncertainty. This paper proposes a ''fuzzy expected value approach'' for data envelopment analysis (DEA) in which fuzzy inputs and fuzzy outputs are first weighted, respectively, and their expected values then used to measure the optimistic and pessimistic efficiencies of decision making units (DMUs) in fuzzy environments. The two efficiencies are finally geometrically averaged for the purposes of ranking and identifying the best performing DMU. The proposed fuzzy expected value approach and its resultant models are illustrated with three numerical examples, including the selection of a flexible manufacturing system (FMS).