@inproceedings{0d481e3c3f834868a414ac8c4b754f2a,
title = "An Analytics Framework to Evaluate Group Decision-Making Capabilities of the Fuzzy Best Worst Method",
abstract = "In the research of multi-attribute decision-making (MADM), the best-worst method is now regarded as promising to support decision-makers in solving discrete multi-criteria decision problems. To extend its capabilities in handling data vagueness and decision makers' ambiguity, the fuzzy best worst method (FBWM) was introduced in 2017. However, its group decision-making capability using different aggregation operators is still under-explored. Therefore, there is a need to systematically compare the performance of different aggregation operators to support future FBWM applications for achieving the group decision-making process. This study proposes a systematic framework to compare five aggregation operators in the fuzzy best-worst method. In contrast, performance metrics for MADM methods are defined to measure the effectiveness of the group decision-making process. Two case studies found that the geometric mean method is the most effective approach to achieving group consensus in the type-1 FBWM, while the max-min method is the worst.",
keywords = "aggregation, comparison, component, fuzzy best-worst method, Group decision-making, performance metrics",
author = "Li, \{Y. L.\} and Tsang, \{Y. P.\} and Wu, \{C. H.\} and Lee, \{C. K.M.\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 5th International Conference on Decision Science and Management, ICDSM 2023 ; Conference date: 03-03-2023 Through 05-03-2023",
year = "2023",
month = mar,
doi = "10.1109/ICDSM59373.2023.00018",
language = "English",
series = "Proceedings - 2023 5th International Conference on Decision Science and Management, ICDSM 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "34--37",
booktitle = "Proceedings - 2023 5th International Conference on Decision Science and Management, ICDSM 2023",
}