Abstract
Multi-attribute decision-making (MADM) can be regarded as a process of selecting the optimal one from all objects. Traditional MADM problems with intuitionistic fuzzy (IF) information are mainly focused on an IF binary relation. However, some complicated problems can not be effectively solved by an IF relation. In order to solve these issues, we set forth two novel decision-making methods that are stated in terms of novel and flexible generalized IF rough set models. For defining these models, four types of IF neighborhoods based on an IF implicator I and an IF triangular norm T are firstly proposed. Secondly, by means of four types of IF neighborhoods, four types of coverings-based generalized IF rough set models are proposed. Furthermore, the relationships among the four types of coverings-based generalized IF rough set models and other types of IF rough set models are also discussed. By means of the principle of the IF-TOPSIS methods, MADM with IF information based on covering-based generalized IF rough sets or based on covering-based generalized fuzzy rough sets is put forward. Finally, we solve MADM problems with the evaluation of IF information based on covering-based generalized IF rough set models. By comparative analysis, we find that the results of this method based on covering-based generalized IF rough set models and based on covering-based generalized fuzzy rough set models are highly consistent. In particular, this method based on covering-based generalized IF rough set models is more effective to deal with MADM than that based on covering-based generalized fuzzy rough set models.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
