Fuzzy efficiency measures in data envelopment analysis using lexicographic multiobjective approach

Adel Hatami-Marbini, Ali Ebrahimnejad, Sebastián Lozano

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)


There is an extensive literature in data envelopment analysis (DEA) aimed at evaluating the relative efficiency of a set of decision-making units (DMUs). Conventional DEA models use definite and precise data while real-life problems often consist of some ambiguous and vague information, such as linguistic terms. Fuzzy sets theory can be effectively used to handle data ambiguity and vagueness in DEA problems. This paper proposes a novel fully fuzzified DEA (FFDEA) approach where, in addition to input and output data, all the variables are considered fuzzy, including the resulting efficiency scores. A lexicographic multi-objective linear programming (MOLP) approach is suggested to solve the fuzzy models proposed in this study. The contribution of this paper is fivefold: (1) both fuzzy Constant and Variable Returns to Scale models are considered to measure fuzzy efficiencies; (2) a classification scheme for DMUs, based on their fuzzy efficiencies, is defined with three categories; (3) fuzzy input and output targets are computed for improving the inefficient DMUs; (4) a super-efficiency FFDEA model is also formulated to rank the fuzzy efficient DMUs; and (5) the proposed approach is illustrated, and compared with existing methods, using a dataset from the literature.

Original languageEnglish
Pages (from-to)362-376
Number of pages15
JournalComputers and Industrial Engineering
Early online date1 Feb 2017
Publication statusPublished - 1 Mar 2017
Externally publishedYes


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