Abstract
While the traditional data envelopment analysis (DEA) requires precise input and output data, available data is usually imprecise and vague. “Fuzzy DEA” integrates the concept of fuzzy set theory with the traditional DEA by representing imprecise and vague data with fuzzy sets. In this paper, a credibility approach is proposed as a way to solve the fuzzy DEA model. The approach transforms a fuzzy DEA model into a well-defined credibility programming model, in which fuzzy variables are replaced by “expected credits” in terms of credibility measures. It is shown that when the membership functions of fuzzy data are trapezoidal, the credibility programming model becomes a linear programming model. Numerical examples are given to illustrate the proposed approach and results are compared with those obtained with alternative approaches.
This research was supported, in part, by the National Textile Center of the United States of America (Grant Number: I01-S01).
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Lertworasirikul, S., Fang, SC., Joines, J.A., Nuttle, H.L.W. (2003). Fuzzy Data Envelopment Analysis: A Credibility Approach. In: Verdegay, JL. (eds) Fuzzy Sets Based Heuristics for Optimization. Studies in Fuzziness and Soft Computing, vol 126. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36461-0_10
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DOI: https://doi.org/10.1007/978-3-540-36461-0_10
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