Data envelopment analysis on a relaxed set of assumptions
Management Science
Fuzzy system reliability analysis using fuzzy number arithmetic operations
Fuzzy Sets and Systems
Fuzzy efficiency measures in data envelopment analysis
Fuzzy Sets and Systems
Fuzzy DEA: a perceptual evalution method
Fuzzy Sets and Systems
A note on the correlation of fuzzy numbers by expected interval
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Data envelopment analysis with imprecise data: an application of Taiwan machinery firms
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software
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
Expert Systems with Applications: An International Journal
Interval efficiency assessment using data envelopment analysis
Fuzzy Sets and Systems
An ideal-seeking fuzzy data envelopment analysis framework
Applied Soft Computing
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
Fuzzy data envelopment analysis: A discrete approach
Expert Systems with Applications: An International Journal
Chance-constrained DEA models with random fuzzy inputs and outputs
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Two-stage DEA: An application to major Brazilian banks
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Data envelopment analysis (DEA) is a linear programming based non-parametric technique for evaluating the relative efficiency of homogeneous decision making units (DMUs) on the basis of multiple inputs and multiple outputs. There exist radial and non-radial models in DEA. Radial models only deal with proportional changes of inputs/outputs and neglect the input/output slacks. On the other hand, non-radial models directly deal with the input/output slacks. The slack-based measure (SBM) model is a non-radial model in which the SBM efficiency can be decomposed into radial, scale and mix-efficiency. The mix-efficiency is a measure to estimate how well the set of inputs are used (or outputs are produced) together. The conventional mix-efficiency measure requires crisp data which may not always be available in real world applications. In real world problems, data may be imprecise or fuzzy. In this paper, we propose (i) a concept of fuzzy input mix-efficiency and evaluate the fuzzy input mix-efficiency using @a - cut approach, (ii) a fuzzy correlation coefficient method using expected value approach which calculates the expected intervals and expected values of fuzzy correlation coefficients between fuzzy inputs and fuzzy outputs, and (iii) a new method for ranking the DMUs on the basis of fuzzy input mix-efficiency. The proposed approaches are then applied to the State Bank of Patiala in the Punjab state of India with districts as the DMUs.