New extension of ordinal priority approach for multiple attribute decision-making problems: design and analysis

Area of Science:

• Engineering
• Decision Science
• Artificial Intelligence

Background:

• Robot selection is complex due to numerous alternatives and conflicting attributes.
• Traditional decision-making methods struggle with the inherent uncertainty and subjective preferences in robot selection.
• Existing approaches often fail to adequately address uncertainty using linguistic variables.

Purpose of the Study:

• To present a novel extended ordinal priority approach within a neutrosophic environment for robot selection.
• To transform linguistic information into triangular neutrosophic numbers using a new scale for attribute and alternative assessment.
• To address the challenge of uncertainty in multi-attribute decision-making for robot selection.

Main Methods:

• Development of a new scale to convert linguistic data into triangular neutrosophic numbers.
• Utilization of the score function of triangular neutrosophic numbers for prioritizing attributes and alternatives.
• Application of the proposed method in a case study for robot selection in Egypt's pharmaceutical industry.

Main Results:

• The proposed neutrosophic approach demonstrates superior performance in robot selection under uncertainty compared to classical and fuzzy methods.
• The method is more time-efficient and simpler than the fuzzy ordinal priority approach.
• Sensitivity and comparative analyses confirmed the robustness and efficiency of the developed approach.

Conclusions:

• The novel neutrosophic ordinal priority approach is effective for robot selection, particularly in uncertain environments.
• The method offers practical benefits for industries seeking to enhance product quality and profitability.
• The approach provides a simpler and faster alternative to existing fuzzy decision-making techniques.