Generalizing linguistic distributions in hesitant decision context

Abstract

The hesitant fuzzy linguistic term set (HFLTS) and the linguistic distribution (LD) are becoming popular tools to describe decision makers' linguistic preferences. By combining HFLTS and LD, this paper proposes a new concept called hesitant linguistic distribution (HLD), and then presents the transformation between HLDs and LDs and the basic comparison and aggregation operations to perform on HLDs. Following, comparisons among several linguistic expressions are made. Finally, the use and behavior mechanism of HLD in multiple attribute group decision making is demonstrated.

Distributed linguistic representations in decision making: Taxonomy, key elements and applications, and challenges in data science and explainable artificial intelligence

“…Chen et al [9], Pang et al [66], Zhang et al [109], etc. In particular, distributed linguistic representations can be formed in two ways:…”

“…Moreover, there exist several representative distributed linguistic representations, including LD with interval symbolic proportions (INLD) [17], possibility distribution for HFLTS (PDHFLTS) [90], and proportional HFLTS (PHFLTS) [9], and hesitant linguistic distribution (HLD) [109]. These variants are introduced below.…”

“…Zhang et al [109] proposed the weighted average operator and ordered weighted average operator for the aggregation of HLDs. Let…”

“…Zhang et al [109] proposed an approach for MAGDM with HLD, in which the transformation between HLDs and LDs and the basic comparison and aggregation operations to perform on HLDs are developed.…”

Constructing Novel Operational Laws and Information Measures for Proportional Hesitant Fuzzy Linguistic Term Sets with Extension to PHFL-VIKOR for Group Decision Making

“…In the previous studies of comparing HFLTSs, Rodríguez et al [1] used the interval values to rank HFLTSs based on the concept of envelope. Zhang et al [36] defined an expectation and a variance function to compare any two hesitant linguistic distributions (HLDs). Zhang et al [9] proposed a comparison method for linguistic distribution assessment based on expectation value and the inaccuracy function.…”