Abstract
Bipolar fuzzy set theory, an extension of fuzzy set theory, deals with incomplete and vague information. The purpose of this research paper is to develop new methodologies in handling multi-criteria decision-making (MCDM) problems where the subjective data given by a decision maker are expressed with bipolar fuzzy information. Every alternative has a rating consists of two parts: positive and negative. The positive part represents the benefit (or satisfaction degree) and the negative part represents the cost (or dissatisfaction degree) of the alternative on the corresponding criterion. In this paper, we present bipolar fuzzy TOPSIS (BF-TOPSIS) method and bipolar fuzzy ELECTRE I (BF-ELECTRE I) method for solving MCDM problems that are equipped with bipolar fuzzy information. We illustrate our proposed methods with examples. We design algorithms of BF-TOPSIS method and BF-ELECTRE I method. We also give comparison of BF-TOPSIS method and BF-ELECTRE I method.