A Fuzzy TOPSIS Method for Robot Selection
By T. Chu, Y. Lin. 2003
368 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/467377911a9b46e506b67c5cb8a0af0a8c042216
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Management Science and Operations Research
- Ian N. Durbach
- T. Stewart
- Ahmad Makui
- Farzad Haghighirad
- Golam Ali Montazer
- Tien-Chin Wang
- Taha M. S. Elhag
- Behzad Ashtiani
In Statistics and Probability
- T. Stewart
- Ian N. Durbach
- Ahmad Makui
- Farzad Haghighirad
- Golam Ali Montazer
- Tien-Chin Wang
- Taha M. S. Elhag
- Behzad Ashtiani
Citations in the corpus
- Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets (2009, background): “Triantaphyllou and Lin [9] develop a fuzzy version of TOPSIS method based on fuzzy arithmetic operations, which leads to a fuzzy relative closeness for each alternative.”
- Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment (2006, methodology): “Chu (2002a; 2002b) and Chu and Lin (2003) also change a fuzzy MCDM problem into a crisp one and solve the crisp MCDM problem using the TOPSIS method.”
- Developing a fuzzy TOPSIS approach based on subjective weights and objective weights (2009, background): “…have been spent and significant advances have been made towards the development of numerous MCDM methodologies for solving different types of problems such as election forecasting (Royes & Bastos, 2001), selecting robots for manufacturing companies (Chu & Lin, 2003) and military threat assessment.”
- Modeling uncertainty in multi-criteria decision analysis (2012, methodology): “Chu and Lin [35] use fuzzy multiplication to obtain fuzzy weighted ratings but then convert”