An Approach to Linguistic Multiple Attribute Decision-Making Based on Unbalanced Linguistic Generalized Heronian Mean...

Area of Science:

• Decision Sciences
• Artificial Intelligence
• Linguistics

Background:

• Multiple attribute decision-making (MADM) often involves linguistic assessment information.
• Existing methods struggle with interactive and unbalanced linguistic data.
• There is a need for robust aggregation operators tailored to such complex scenarios.

Purpose of the Study:

• To propose novel unbalanced linguistic generalized Heronian mean aggregation operators.
• To address linguistic MADM problems with interactive unbalanced assessment information.
• To develop methods that account for varying importance of input arguments.

Main Methods:

• Development of unbalanced linguistic generalized arithmetic and geometric Heronian mean operators.
• Introduction of weighted versions to handle attribute importance.
• Investigation of operator properties and specific cases.
• Application to a low-carbon tourist decision-making problem.

Main Results:

• The proposed operators effectively aggregate unbalanced linguistic assessment information.
• The approach demonstrates effectiveness and universality in practical decision-making.
• Sensitivity analysis confirms the robustness of the developed methods.

Conclusions:

• The novel aggregation operators provide a powerful tool for linguistic MADM with unbalanced data.
• The proposed methods offer a reliable and flexible approach for complex decision scenarios.
• The study validates the practical applicability and robustness of the new operators.