Multicriteria Group Decision Making Based on Intuitionistic Normal Cloud and Cloud Distance Entropy.

Area of Science:

• Decision Sciences
• Information Science
• Artificial Intelligence

Background:

• Uncertainty in information is a critical challenge in decision-making.
• Randomness and fuzziness are primary sources of uncertainty.
• Existing methods may suffer information loss or distortion.

Purpose of the Study:

• To propose a novel multicriteria group decision-making (MCGDM) method.
• To address information uncertainty using intuitionistic normal clouds and cloud distance entropy.
• To improve the accuracy and reliability of decision-making processes.

Main Methods:

• Developed a backward cloud generation algorithm for intuitionistic normal clouds.
• Introduced cloud distance entropy by integrating cloud model distance measurement with information entropy.
• Defined distance measurement for intuitionistic normal clouds and proposed a criterion weight determination method.
• Extended the VIKOR method to the intuitionistic normal cloud environment.

Main Results:

• Transformed intuitionistic fuzzy decision information into an intuitionistic normal cloud matrix, preserving information integrity.
• Established a robust method for determining criterion weights under uncertainty.
• Obtained reliable ranking results for alternatives using the extended VIKOR method.
• Demonstrated the method's effectiveness and practicality through two numerical examples.

Conclusions:

• The proposed method effectively handles uncertainty in MCGDM problems.
• Intuitionistic normal clouds and cloud distance entropy offer a powerful framework for decision analysis.
• The method provides a practical and effective solution for complex decision-making scenarios.