Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment

By Yingming Wang, Taha M. S. Elhag. Expert systems with applications, 2006

944 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/52361ace741b7f9ecf14b2f0c32423cade1954c7

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In Civil and Structural Engineering

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Methods for aggregating intuitionistic uncertain linguistic variables and their application to group decision making (2012, methodology): “Many ranking methods of MADM, such as the TOPSIS method [14,17,40,51], the grey relational analysis method [23,43], the VIKOR method [19,25– 27], the PROMETHEE method [6,24], and the ELECTRE method [21,29,30], have been extended to fit different attribute types, such as interval numbers, triangular ”
A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods (2010, methodology): “Wang and Elhag (2006) proposed a fuzzy TOPSIS method based on alpha level sets and presents a non-linear programming solution procedure.”
Multiple criteria construction management decisions considering relations between criteria (2010, methodology): “…occur the consequences of which can be very damaging to the project, assessment of investment risk and construction risk is widely performed by applying usual and extended TOPSIS or other multiple criteria decision making methods (Wang and Elhag 2006; Zavadskas et al. 2008; Shevchenko et al. 2008).”
A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach (2008, methodology): “As indicated by the literature review, the FST is incorporated into many concepts and procedures when enhancing their capability to handle MADM problems in fuzzy environments (Chen et al., 2006; Chang & Wang, 2006; Kuo & Chen, 2004; Lee, Lau, Liu, & Tam, 2001; Wang & Elhag, 2006).”
Modeling uncertainty in multi-criteria decision analysis (2012, methodology): “Their method preserves fuzziness right up to the ranking stage, but Wang and Elhag [142] find that this results in the supports of the fuzzy relative idealities being overexaggerated.”
A fuzzy multi-criteria decision making model for supplier selection (2011, background): “…should have the shortest distance from the Positive Ideal Solution (PIS), i.e., the solution that maximizes the benefit criteria; and the farthest from the Negative Ideal Solution (NIS), i.e., the solution that minimizes the benefit criteria (Benitez, Martin, & Roman, 2007; Wang & Elhag, 2006).”