Double-hierarchy hesitant fuzzy linguistic term set-based decision framework for multi-attribute group decision-making

With massive growth in decision-making theory, representation of preference information plays an indispensable role. To rationally handle uncertainty, scholars presented different ideas of which hesitant fuzzy linguistic term set (HFLTS) is a good choice to represent hesitancy in decision makers' (DMs) preferences. The challenge with HFLTS is that it cannot be used for representing complex linguistic terms. To better circumvent this challenge, double-hierarchy HFLTS (DHHFLTS) is presented. Motivated by the power of DHHFLTS in expressing complex linguistic terms by using two linguistic hierarchies, a decision framework is proposed under the DHHFLTS context. Initially, the framework presents a new aggregation operator called simple double-hierarchy frequency match aggregation operator for sensible aggregation of DMs' preference information. Later, the mathematical programming model is extended under the DHHLTS context for rational estimation of attribute weight with partially known information. Also, the popular Vise Kriterijumska Optimizacija Kompromisno Resenje ranking method is extended under the DHHFLTS context for the selection of a suitable object from the set of objects. Finally, the proposed decision framework is validated for its practicality by demonstrating two numerical examples viz., green supplier selection problem and renewable energy source selection problem. Also, the strengths and weaknesses of the proposed framework are realized by comparison with other methods.

Automated summary

The article introduces HFLTS and DHHFLTS for handling decision makers' hesitant preferences. Through DHHFLTS, new aggregation operator and mathematical programming model are proposed for rational estimation of attribute weight. Meanwhile, the popular ranking method is extended under the DHHFLTS context.