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 $(0,1)$ 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.

一个数学模型要想以最理想的方式表达遇到的不确定性问题，就必须能够以最准确的方式表达问题中的参数与对象之间的关系。在本文中，考虑到双极软集理论，因为它也处理参数集中参数的负参数。本文的主要目的是通过最小化决策者的有效性来确定参数和对象之间的隶属度，从而建立一个令人印象深刻的决策方法。为此，定义了“双极关系隶属函数”和“非双极关系隶属函数”的概念，并给出了一些重要的性质。由于这些提议的概念，$(0,1)$可以确定。最后，我们提出的决策方法与文献中先前介绍的其他决策方法的区别已经清楚地表明，并进行了比较分析。