Fuzzy AHP to determine the relative weights of evaluation criteria and Fuzzy TOPSIS to rank the alternatives
By Fatemeh Torfi, Reza Zanjirani Farahani, Shabnam Rezapour. Applied Soft Computing, 2010
413 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Management of Technology and Innovation, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/6266942ac39c96b4bbbb3789a82111ec7f36320a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- X. Ming
- Zhenyong Wu
- Young Chel Kwun
- J. H. Park
- Il Young Park
- Tossapol Kiatcharoenpol
- Xuegong Tan
- Wenyan Song
In Management of Technology and Innovation
In Management Science and Operations Research
- X. Ming
- Zhenyong Wu
- Young Chel Kwun
- J. H. Park
- Il Young Park
- Tossapol Kiatcharoenpol
- Xuegong Tan
- Wenyan Song
Citations in the corpus
- An integrated rough number-based approach to design concept evaluation under subjective environments (2013, methodology): “When the criteria importance judgements and performance ratings are vague and inaccurate, the fuzzy AHP and fuzzy TOPSIS are the preferred techniques (Torfi et al. 2009).”
- Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment (2011, methodology): “AHP has been extensively applied by academics and professionals [12,14–20].”
- Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers (2018, background): “Torfi, Farahani, and Rezapour (2010) proposed fuzzy AHP and fuzzy TOPSIS to evaluate the alternative options in respect to the user’s preference orders.”