A Novel q-Rung Dual Hesitant Fuzzy Multi-Attribute Decision-Making Method Based on Entropy Weights

Area of Science:

• Decision Sciences
• Fuzzy Set Theory
• Information Fusion

Background:

• Multiple attribute decision-making (MADM) problems often involve uncertainty and vagueness.
• Existing fuzzy decision-making methods may not fully capture complex uncertainty under q-rung dual hesitant fuzzy environments.

Purpose of the Study:

• To propose a novel MADM method utilizing aggregation operators within a q-rung dual hesitant fuzzy set (q-RDHFS) framework.
• To develop new aggregation operators and an entropy measure tailored for q-RDHFSs.
• To address MADM problems where decision-maker weights are unknown.

Main Methods:

• Development of novel aggregation operators for q-rung dual hesitant fuzzy sets (q-RDHFSs).
• Introduction of a new entropy measure to determine weight information for aggregated q-RDHFS elements.
• Formulation of a new MADM method designed for situations with completely unknown weight information.

Main Results:

• The proposed aggregation operators and entropy measure are effective for q-RDHFSs.
• The novel MADM method demonstrates effectiveness and superior performance in numerical examples.
• Comparative analysis validates the advantages of the new MADM method over existing approaches.

Conclusions:

• The developed MADM method provides a robust tool for decision-making under q-rung dual hesitant fuzzy environments.
• The method is particularly useful for practical MADM problems where attribute weights are not predetermined.
• This research contributes a valuable approach to enhancing decision-making processes in complex uncertain scenarios.