Multiple criteria decision analytic methods in management with T-spherical fuzzy information

Area of Science:

• Decision Sciences
• Fuzzy Set Theory
• Operations Research

Background:

• Traditional fuzzy sets struggle with complex uncertainties, leading to information loss.
• T-spherical fuzzy (T-SF) sets offer a more robust framework using four membership grades to capture nuanced uncertainty.
• Existing multi-criteria decision-making (MCDA) methods require enhancement for highly uncertain environments.

Purpose of the Study:

• To develop a novel appraisal mechanism and decision analytic methodology based on T-spherical fuzzy (T-SF) sets.
• To introduce T-SF correlation-oriented measurements and the T-SF Minkowski distance index for enhanced MCDA.
• To address complex decision problems involving uncertainty and multiple criteria.

Main Methods:

• Development of T-SF correlation-oriented measurements utilizing maximum and square root functions.
• Integration of outranking/outranked identifiers using the T-SF Minkowski distance index.
• Construction of T-SF decision analytic procedures incorporating appraisal significance, maximizing/minimizing, and global outranking indices.

Main Results:

• A new T-SF decision analytic methodology and appraisal mechanism were successfully developed and validated.
• The proposed method demonstrated effectiveness in a concrete location selection problem, showing adaptability and reliability.
• Sensitivity analyses confirmed the robustness of the derived predominance relationships among options.

Conclusions:

• The T-SF decision analytic methodology provides a reliable and flexible approach for decision-making under uncertainty.
• The fixed ideal/anti-ideal benchmarking mechanism is recommended for its ease of use and sensitivity.
• The research advances computational tools for handling T-SF information in complex decision scenarios.