Performance evaluation in stochastic environments using mean-variance data envelopment analysis

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Traditional Data Envelope Analysis (DEA) neglects uncertainty for the input-output variables by treating the observations as if they were the true input-output variables to select reference units for efficiency estimation and performance benchmarking. In stochastic environments, the traditional framework may include stochastically dominated reference units and exclude stochastically undominated ones. To incorporate uncertainty for the input-output variables in DEA, we propose a mean-variance framework derived from the theory of stochastic dominance. From that framework an extension to the traditional model is derived that prevents the selection of stochastically dominated reference units. In addition, within the mean-variance approach, variance restrictions can be specified that reduce the uncertainty for the performance of the evaluated unit relative to its reference unit.

Original languageEnglish
Pages (from-to)281-292
Number of pages12
JournalOperations Research
Volume49
Issue number2
Publication statusPublished - Mar 2001
Externally publishedYes

Fingerprint

Data envelopment analysis
Uncertainty analysis
Benchmarking
Uncertainty
Mean-variance
Performance evaluation
Data envelope analysis

ASJC Scopus subject areas

  • Management Science and Operations Research

Cite this

Performance evaluation in stochastic environments using mean-variance data envelopment analysis. / Post, T.

In: Operations Research, Vol. 49, No. 2, 03.2001, p. 281-292.

Research output: Contribution to journalArticle

@article{3a0e908cf27343ebab9367944ea14039,
title = "Performance evaluation in stochastic environments using mean-variance data envelopment analysis",
abstract = "Traditional Data Envelope Analysis (DEA) neglects uncertainty for the input-output variables by treating the observations as if they were the true input-output variables to select reference units for efficiency estimation and performance benchmarking. In stochastic environments, the traditional framework may include stochastically dominated reference units and exclude stochastically undominated ones. To incorporate uncertainty for the input-output variables in DEA, we propose a mean-variance framework derived from the theory of stochastic dominance. From that framework an extension to the traditional model is derived that prevents the selection of stochastically dominated reference units. In addition, within the mean-variance approach, variance restrictions can be specified that reduce the uncertainty for the performance of the evaluated unit relative to its reference unit.",
author = "T. Post",
year = "2001",
month = "3",
language = "English",
volume = "49",
pages = "281--292",
journal = "Operations Research",
issn = "0030-364X",
publisher = "INFORMS Inst.for Operations Res.and the Management Sciences",
number = "2",

}

TY - JOUR

T1 - Performance evaluation in stochastic environments using mean-variance data envelopment analysis

AU - Post, T.

PY - 2001/3

Y1 - 2001/3

N2 - Traditional Data Envelope Analysis (DEA) neglects uncertainty for the input-output variables by treating the observations as if they were the true input-output variables to select reference units for efficiency estimation and performance benchmarking. In stochastic environments, the traditional framework may include stochastically dominated reference units and exclude stochastically undominated ones. To incorporate uncertainty for the input-output variables in DEA, we propose a mean-variance framework derived from the theory of stochastic dominance. From that framework an extension to the traditional model is derived that prevents the selection of stochastically dominated reference units. In addition, within the mean-variance approach, variance restrictions can be specified that reduce the uncertainty for the performance of the evaluated unit relative to its reference unit.

AB - Traditional Data Envelope Analysis (DEA) neglects uncertainty for the input-output variables by treating the observations as if they were the true input-output variables to select reference units for efficiency estimation and performance benchmarking. In stochastic environments, the traditional framework may include stochastically dominated reference units and exclude stochastically undominated ones. To incorporate uncertainty for the input-output variables in DEA, we propose a mean-variance framework derived from the theory of stochastic dominance. From that framework an extension to the traditional model is derived that prevents the selection of stochastically dominated reference units. In addition, within the mean-variance approach, variance restrictions can be specified that reduce the uncertainty for the performance of the evaluated unit relative to its reference unit.

UR - http://www.scopus.com/inward/record.url?scp=0035268972&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035268972&partnerID=8YFLogxK

M3 - Article

VL - 49

SP - 281

EP - 292

JO - Operations Research

JF - Operations Research

SN - 0030-364X

IS - 2

ER -