Data envelopment analysis with imprecise data

The conventional data envelopment analysis (DEA) measures the relative efficiencies of a set of decision making units (DMUs) with exact value of inputs and outputs. For imprecise data, i.e., mixtures of interval data and ordinal data, some methods have been developed to calculate the interval of the efficiency scores. This paper constructs a procedure to measure the efficiencies of DMUs with mixtures of interval data, ordinal data and fuzzy data. The basic idea is to transform all data to fuzzy data and transform a fuzzy DEA model to a family of conventional crisp DEA models by applying the α -cut approach. A pair of parametric programs is formulated to describe that family of crisp DEA models, via which the membership function of the efficiency measure is derived. Since the efficiency measures are expressed by membership functions rather than by crisp values, more information is provided for management. By extending to fuzzy environment, the DEA approach is made more powerful for applications.