Performance　assessment　often　has　to　be　conducted　under　uncertainty.　This　paper　proposes　a　＇fuzzy　expected　value　approach＇　for　data　envelopment　analysis　(DEA)　in　which　fuzzy　inputs　and　fuzzy　outputs　are　first　weighted,　respectively,　and　their　expected　values　then　used　to　measure　the　optimistic　and　pessimistic　efficiencies　of　decision　making　units　(DMUs)　in　fuzzy　environments.　The　two　efficiencies　are　finally　geometrically　averaged　for　the　purposes　of　ranking　and　identifying　the　best　performing　DMU.　The　proposed　fuzzy　expected　value　approach　and　its　resultant　models　are　illustrated　with　three　numerical　examples,　including　the　selection　of　a　flexible　manufacturing　system　(FMS).　©　2011　Elsevier　Ltd.　All　rights　reserved.