Ranking DMUs with interval data using interval super efficiency index

Data Envelopment Analysis (DEA) requires that the data for all inputs and outputs are known exactly. When some outputs and inputs are known decision variables, such as interval data and ordinal data, the DEA models becomes a nonlinear programming problem and is called imprecise DEA (IDEA). When data assume to be interval, decision making units (DMUs) can divide to three classes as follows: (I) efficient in any cases, (II) efficient in maximal sense and inefficient in minimal sense, (III) always inefficient. In this paper, we discuss a new technique for assessing the sensitivity of efficiency and inefficiency classifications in DEA with interval data in above three cases. Also we find radius of stability for all DMUs. Available bank branch data in Iran was used to illustrate the applicability of this approach Mathematics Subject Classification: Operations Research, 90

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Computing Efficiency for Decision Making Units with Negative and Interval Data

Piri¹,

Givehchi²,

Keshavarz³

2016

Data Envelopment Analysis and Decision Science

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Data Envelopment Analysis (DEA) is a nonparametric method for identifying sources and estimating the mount of inefficiencies contained in inputs and outputs produced by Decision Making Units (DMUs). DEA requires that the data for all inputs and outputs should be known exactly, but under many qualifications, exact data are inadequate to model real-life situations. So these data may have different structures such as bounded data, interval data, and fuzzy data. Moreover, the main assumption in all DEA is that input and output values are positive, but we confront many cases that discount this condition producing negative data. The purpose of this paper is to compute efficiency for DMUs, which permits the presence of intervals which can take both negative and positive values.

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Ranking DMUs with interval data using interval super efficiency index

Cited by 3 publications

References 6 publications

How to assess sustainability of suppliers in volume discount context? A new data envelopment analysis approach

How to assess sustainability of suppliers in volume discount context? A new data envelopment analysis approach

Sensitivity analysis of units with interval data in DEA

Computing Efficiency for Decision Making Units with Negative and Interval Data

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