Abstract
The original context-dependent Data Envelopment Analysis (DEA) is developed to measure the attractiveness and progress of Decision-Making Units (DMUs) based on a given evaluation context and different strata of efficient frontiers, rather than the traditional first-level efficient frontier, are used as evaluation contexts. It is limited to crisp data. To deal with imprecise data, this paper introduces the notion of fuzziness and develops a procedure to provide finer evaluation results of DMUs with fuzzy observations based on the original context- dependent DEA by using a ranking method based on the comparison of α-cuts. The proposed approach is an extension to the fuzzy environment of the original context-dependent DEA; it represents some real-life processes more appropriately. A numerical example is used to illustrate the approach.
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