Similarity Degree for Multi-Attribute Decision Making with Incomplete Dual Hesitant Fuzzy Sets

Abstract

Due to the uncertainty and fuzziness of the real world, the dual hesitant fuzzy set (DHFS) has been proposed to express uncertain information during the process of multi-attribute decision making (MADM). In order to process the information with incomplete dual hesitant fuzzy elements (IDHFEs) in MADM, a new similarity degree for MADM with incomplete dual hesitant fuzzy sets (IDHFSs) is proposed. The concept of similarity degree of IDHFEs and similarity aggregation matrix are introduced. Then a complete dual hesitant fuzzy matrix (CDHFM) is obtained by using the maximum similarity to complement the data. An investment selection example is provided to illustrate the validity and applicability of the proposed method.