Fuzzy outranking for environmental assessment. Case study: iron and steel making industry

By Jutta Geldermann, Thomas Stefan Spengler, O. Rentz. Fuzzy Sets Syst., 2000

312 citations recorded by the source corpus.

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Using fuzzy numbers to propagate uncertainty in matrix-based LCI (2008, methodology): “There has also been some work on the use of fuzzy set theory to model the variability of data and decision-making uncertainty in LCA ( Geldermann et al. 2000; Tan et al. 2002, 2004; Benetto et al. 2006; Guereca et al. 2007 ;S eppala 2007).”
An Extension of the Electre I Method for Group Decision-Making Under a Fuzzy Environment (2011, methodology): “The outranking methods as a special subgroup of MCDA methods are particularly suitable for integral decision making through the notion of weak preference and incomparability, which better represent the real decision situation [18,19].”
A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making (2011, background): “Recently, fuzzy set theory has been applied to a wide range of fields such as management science, decision theory, artificial intelligence, computer science, expert systems, logic, control theory and statistics, among others (Chen, 2001; Chen and Tzeng, 2004; Chiou et al., 2005; Ding and Liang, 2005”