Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Profitability and Marketability of the Top 55 U.S. Commercial Banks
Management Science - Special issue on the performance of financial Institutions
Fuzzy efficiency measures in data envelopment analysis
Fuzzy Sets and Systems
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Measuring Information Technology's Indirect Impact on Firm Performance
Information Technology and Management
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 data envelopment analysis (DEA): Model and ranking method
Journal of Computational and Applied Mathematics
Fuzzy data envelopment analysis and its application to location problems
Information Sciences: an International Journal
Evaluation of information technology investment: a data envelopment analysis approach
Computers and Operations Research
Efficiency measurement for network systems: IT impact on firm performance
Decision Support Systems
Self-organizing fuzzy aggregation models to rank the objects with multiple attributes
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Efficiency of parallel production systems with fuzzy data
Fuzzy Sets and Systems
Computing fuzzy process efficiency in parallel systems
Fuzzy Optimization and Decision Making
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Two-stage DEA (data envelopment analysis) models show the performance of individual processes, and thus are more informative than the conventional one-stage models for making decisions. This paper extends this approach from deterministic to uncertain situations, where the observations are represented by fuzzy numbers. The extension principle is utilized to develop a pair of two-level mathematical programs to calculate the lower and upper bounds of the @a-cut of the fuzzy efficiency. By enumerating various values of @a, the membership functions of fuzzy efficiencies are constructed numerically. It is found that the property of the system efficiency being equal to the product of the two process efficiencies, which holds for the deterministic case, also holds for the fuzzy case. This property can be generalized to series systems with more than two processes. An example of non-life insurance companies in Taiwan is used to explain how to calculate the system and process efficiencies and how to derive their relationship when the data is fuzzy.