A procedure for ranking efficient units in data envelopment analysis
Management Science
Robust Solutions to Least-Squares Problems with Uncertain Data
SIAM Journal on Matrix Analysis and Applications
Mathematics of Operations Research
An application of data envelopment analysis in telephone offices evaluation with partial data
Computers and Operations Research
Fuzzy efficiency measures in data envelopment analysis
Fuzzy Sets and Systems
Fuzzy DEA: a perceptual evalution method
Fuzzy Sets and Systems
Robust Solutions to Uncertain Semidefinite Programs
SIAM Journal on Optimization
Idea and Ar-Idea: Models for Dealing with Imprecise Data in Dea
Management Science
Operations Research
Data envelopment analysis with missing values: an interval DEA approach
Applied Mathematics and Computation
Interval efficiency assessment using data envelopment analysis
Fuzzy Sets and Systems
A robust optimization approach for imprecise data envelopment analysis
Computers and Industrial Engineering
An ideal-seeking fuzzy data envelopment analysis framework
Applied Soft Computing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Robust solutions of uncertain linear programs
Operations Research Letters
Robust linear optimization under general norms
Operations Research Letters
Fuzzy stochastic data envelopment analysis with application to base realignment and closure (BRAC)
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
Data envelopment analysis (DEA) is a non-parametric method for measuring the relative efficiency of a set of decision making units using multiple precise inputs to produce multiple precise outputs. Several extensions to DEA have been made for the case of imprecise data, as well as to improve the robustness of the assessment for these cases. Prevailing robust DEA (RDEA) models are based on mirrored interval DEA models, including two distinct production possibility sets (PPS). However, this approach renders the distance measures incommensurate and violates the standard assumptions for the interpretation of distance measures as efficiency scores. We propose a modified RDEA (MRDEA) model with a unified PPS to overcome the present problem in RDEA. Based on a flexible formulation for the number of variables perturbed, MRDEA calculates the empirical distribution for the interval efficiency for the case of a random number of variables affected. The MRDEA approach also decreases the computational complexity of the RDEA model, as well as significantly increases the discriminatory power of the model without additional information requirements. The properties of the method are demonstrated for four different numerical instances.