A crossefficiency evaluation approach in fuzzy data envelopment analysis

Cross-efficiency evaluation is an effective approach to ranking decision making units (DMUs) that utilizemultiple inputs to produce multiple outputs. This paper proposes a novel fuzzy data envelopment analysis (FDEA) framework for cross-efficiency evaluation that handles fuzzy input and output data. To do this, two fuzzy virtual DMUs namelyfuzzy ideal DMU (FIDMU) and fuzzy anti-ideal DMU (FADMU) are introduced. The new fuzzy DEA model determinesfuzzy input and output weights from the point of view of maximization of the relative closeness of each DMU with respect to FIDMUandFADMU.