Theapplicationofdataenvelopmentanalysis(DEA)asamultiplecriteriadecisionmaking(MCDM)techniquehasbeengainingmoreandmoreattentioninrecentresearch.InthepracticeofapplyingDEAapproach,theappearanceofuncertaintiesoninputandoutputdataofdecisionmakingunit(DMU)mightmakethenominalsolutioninfeasibleandleadtotheefficiencyscoresmeaninglessfrompracticalview.ThispaperanalyzestheimpactofdatauncertaintyontheevaluationresultsofDEA,andproposesseveralrobustDEAmodelsbasedontheadaptationofrecentlydevelopedrobustoptimizationapproaches,whichwouldbeimmuneagainstinputandoutputdatauncertainties.TherobustDEAmodelsdevelopedarebasedoninput-orientedandoutputorientedCCRmodel,respectively,whentheuncertaintiesappearinoutputdataandinputdataseparately.Furthermore,therobustDEAmodelscoulddealwithrandomsymmetricuncertaintyandunknown-but-boundeduncertainty,inbothofwhichthedistributionsoftherandomdataentriesarepermittedtobeunknown.TherobustDEAmodelsareimplementedinanumericalexampleandtheefficiencyscoresandrankingsofthesemodelsarecompared.TheresultsindicatethattherobustDEAapproachcouldbeamorereliablemethodforefficiencyevaluationandrankinginMCDMproblems.
