Sensitivity analysis of units with interval data in DEA

Izadikhah, Mohammad; Lotfı, Farhad Hosseinzadeh

doi:10.12988/imf.2007.07188

Cited by 2 publications

(1 citation statement)

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“…Contrarily to IDEA their transformation on the variables are made on the basis of the original data set, without applying any scale transformations on data. Also, in Jahanshahloo et al [12,13] the radius of stability for the DMUs with interval data is calculated. In this paper we concentrate on the context-dependent DEA with interval data.…”

Section: Introductionmentioning

confidence: 99%

Context-Dependent Data Envelopment Analysis with Interval Data

Izadikhah¹

2011

AJCM

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Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. The context-dependent DEA is introduced to measure the relative attractiveness of a particular DMU when compared to others. In real-world situation, because of incomplete or non-obtainable information, the data (Input and Output) are often not so deterministic, therefore they usually are imprecise data such as interval data, hence the DEA models becomes a nonlinear programming problem and is called imprecise DEA (IDEA). In this paper the context-dependent DEA models for DMUs with interval data is extended. First, we consider each DMU (which has interval data) as two DMUs (which have exact data) and then, by solving some DEA models, we can find intervals for attractiveness degree of those DMUs. Finally, some numerical experiment is used to illustrate the proposed approach at the end of paper.

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Section: Introductionmentioning

confidence: 99%