Consistent and robust ranking in imprecise data envelopment analysis under perturbations of random subsets of data

Amir H. Shokouhi, Hamid Shahriari, Per J. Agrell, Adel Hatami-Marbini

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)133-160
Number of pages28
JournalOR Spectrum
Volume36
Issue number1
Early online date20 Jul 2013
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

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