Extended fuzzy N-Soft PROMETHEE method and its application in robot butler selection

Area of Science:

• Decision Sciences
• Operations Research
• Artificial Intelligence

Background:

• Multi-criteria group decision-making (MCGDM) often involves uncertainty and ambiguity.
• Existing fuzzy PROMETHEE methods address ambiguity but may not fully capture complex human uncertainty.
• There is a need for advanced techniques to handle generalized uncertainty in decision-making.

Purpose of the Study:

• To propose a novel fuzzy N-soft PROMETHEE technique for enhanced decision-making under uncertainty.
• To extend the capabilities of the PROMETHEE method by incorporating N-grading in fuzzy parameters.
• To demonstrate the practicality and superiority of the proposed technique compared to existing methods.

Main Methods:

• Extension of the PROMETHEE technique to incorporate N-grading in fuzzy parametric descriptions.
• Utilization of the Analytic Hierarchy Process (AHP) for validating standard weights.
• Development and explanation of the fuzzy N-soft PROMETHEE method with a detailed flowchart.

Main Results:

• The fuzzy N-soft PROMETHEE technique effectively ranks alternatives in the presence of generalized uncertainty.
• A practical application demonstrated the method's feasibility in selecting robot housekeepers.
• Comparison showed increased confidence and accuracy compared to the standard fuzzy PROMETHEE method.

Conclusions:

• The fuzzy N-soft PROMETHEE technique offers a robust approach to MCGDM with generalized uncertainty.
• The method provides a more accurate and confident decision-making framework.
• This work contributes a significant advancement to the field of fuzzy decision-making techniques.