Pythagorean uncertain linguistic partitioned Bonferroni mean operators and their application in multi-attribute decision making

Abstract

In this paper, a new extension of linguistic term sets called Pythagorean uncertain linguistic sets (PULSs) is defined, which is based on uncertain linguistic term sets and Pythagorean fuzzy sets (PFSs) originally introduced by Yager [41]. This new fuzzy linguistic set has a greater membership repr esentation space and a more powerful expression ability than the intuitionistic uncertain linguistic sets (IULSs) proposed by Liu [18]. Based on the defined operations of PULSs, the Pythagorean uncertain linguistic Bonferroni mean (PULBM) operator and its weighted form are developed to capture the interrelationship among attributes. Taking into account that the classical BM operator is based on the assumption that all attributes are interrelated, and such assumption does not always exist in most of the practical decision-making situations, we further present a Pythagorean uncertain linguistic Partitioned Bonferroni mean (PULPBM) operator and its weighted form (WPULPBM) to solve such situations where all attributes may be divided into several different categories based on specific correlation characteristics and members in the same category are interrelated while no correlation exists among different categories. Then, based on the WPULPBM operator, an approach for MADM problems with Pythagorean uncertain linguistic information is proposed. Finally, a practical example is given to illustrate the application of the proposed approach and comparison analysis is investigated with other existing methods to show the effectiveness of the proposed approach.