A consensus model with bipolar fuzzy archimedean-dombi operators for group decision-making

This paper presents a new approach for consensus-based group decision making using bipolar fuzzy sets (BFSs). BFSs are effective tools for handling bipolarity and fuzziness, and they have been applied in various fields such as artificial intelligence, data science, and economics. However, there are currently no aggregation operators (AOs) that can aggregate criteria values in a bipolar fuzzy (BF) environment with the desired flexibility and generality, nor any consensus-reaching process proposed to capture the uncertainty of information when some experts are biased. To address these issues, the paper proposes bipolar fuzzy (BF) Archimedean-Dombi weighted aggregation operators (AOs) based consensus decision-making approach. The BF Archimedean-Dombi weighted averaging (BFADWA) and BF Archimedean-Dombi weighted geometric (BFADWG) AOs are proposed, and a consensus-based group decision-making methodology is developed. The criteria weights are calculated using Grey Correlation Coefficient. A case study with four experts is conducted to assess four Open-Source Software Learning Management Systems over a range of nine criteria. To confirm the consistency of the methodology, a comparative study is performed with other existing tools based on BF Dombi weighted averaging and geometric AOs, BF Hamacher weighted averaging and geometric AOs. Results show that the method generates the same ranking order and is more realistic and flexible. The proposed method can be applied to various fields where consensus-based group decision-making is required, providing a new perspective for handling uncertainty and bias in decision-making.