A characterization of the extension principle
Fuzzy Sets and Systems - Special issue: Dedicated to the memory of Richard E. Bellman
Linear programming and network flows (2nd ed.)
Linear programming and network flows (2nd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy efficiency measures in data envelopment analysis
Fuzzy Sets and Systems
A simple approximation of productivity scores of fuzzy production plans
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
A robust optimization approach for imprecise data envelopment analysis
Computers and Industrial Engineering
A study of developing an input-oriented ratio-based comparative efficiency model
Expert Systems with Applications: An International Journal
Using the DEA-R model in the hospital industry to study the pseudo-inefficiency problem
Expert Systems with Applications: An International Journal
Exploration of efficiency underestimation of CCR model: Based on medical sectors with DEA-R model
Expert Systems with Applications: An International Journal
Fuzzy data envelopment analysis: A fuzzy expected value approach
Expert Systems with Applications: An International Journal
Short communication: Fuzzy data envelopment analysis models with assurance regions: A note
Expert Systems with Applications: An International Journal
A concept of fuzzy input mix-efficiency in fuzzy DEA and its application in banking sector
Expert Systems with Applications: An International Journal
International Journal of Fuzzy System Applications
Computers and Industrial Engineering
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Data envelopment analysis (DEA) allows individual decision-making unit (DMU) to select the weights that are most favorable to them in calculating the ratio of the aggregated output to the aggregated input. The concept of the assurance region (AR) is restricting the ratio of any two weights to some range to avoid the evaluated DMUs from ignoring or relying too much on any criterion in evaluation. In this paper we develop a fuzzy DEA/AR method that is able to calculate the fuzzy efficiency score when the input and output data are represented as convex fuzzy numbers. Based on Zadeh's extension principle, a pair of two-level mathematical programs is formulated to calculate the lower and upper bounds of the fuzzy efficiency score. We transform this pair of two-level mathematical programs into a pair of conventional DEA/AR method to derive the bounds of the efficiency. The dual models of the fuzzy DEA/AR for efficiency improvement are also considered. To illustrate how the proposed method is applied, the measurement of the efficiency of the university libraries in Taiwan with fuzzy observations is exemplified.