Abstract
In order for a mathematical model to express the uncertainty problems encountered in the most ideal way, it must be able to express the relationships between the parameters and objects in the problem in the most accurate way. In this paper, the bipolar soft set theory is taken into consideration since it also deals with the negative parameters of parameters in a parameter set. The main purpose of the paper is to determine the membership degrees between parameters and objects by minimising the effectiveness of the decision maker, and thus to build an impressive decision-making approach. For this, the concepts ‘Bipolar Relational Membership Function’ and ‘NOT Bipolar Relational Membership Function’ are defined and some important properties are given. Thanks to these proposed concepts, it is ensured that the decision maker is asked to express only firm judgments as 0 and 1, and the membership degrees between can be determined. Finally, the difference of our proposed decision-making approach from other decision-making approaches previously introduced in the literature has been clearly demonstrated and a comparative analysis has been made.
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.
